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BUGSPHUSE US 2020 Papers - Index
186 papers. Source: PHUSE (Pharmaceutical Users Software Exchange) US Connect conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | 2020 Syneos Health. All rights reserved | Working Smarter | protocol deviations, subject visits, proc freq, outlier, concomitant medications | source | 7 | |
| 2 | A Case Study: Data Transparency of Clinical Trial Data | Sherry Meeh | Data Transparency (DT) of clinical trial data is in high demand in today's world of increasing research needs and new regulations across the globe, including US Food and Drug Administration Amendments Act of 2007(FDAAA) regulations of protocol/result disclosure in clinicaltrials.gov; US California Consumer Privacy Act (CCPA); EU Clinical Trials Regulation 536/2014(CTR) / EudraCT requirements; European Medicine Agent Policy on Publication of Clinical Data for Medicinal Products for Human Use (or, EMA Policy 0070); EU General Data Protection Regulation (GDPR); Health Canada's Public Release of Clinical Information (PRCI); and Japan Pharmaceutical Manufacturers Association (JPMA) Information on Japanese Regulatory Affairs. Recently, in 2019, the FDA has concluded the Clinical Data Summary Pilot Program and is proposing a new integrated review template. Therefore, a holistic DT process is needed to face the challenge of the complexity of different regulations and data utility needs | clinical trial, clinical data, trial summary, phase i, data protection, threshold | source presentation source |
8 |
| 3 | A Collaborative Approach to Analysis Standards | Margaret Merlino, Marleen Nijs |
When is a standard not a standard? When no one uses it! While that may be a bit exaggerated, to mitigate this possibility Janssen has developed a collaborative model for not only creating new analysis standards for the Janssen study teams but also for correcting and enhancing them. The standards include templates for select study documents, analysis dataset metadata and template code, and code to produce tables, listings and graphs. These are aligned with CDISC standards. The ongoing effort to keep these standards relevant and useable by our study teams involves the teamwork from representatives from three areas: the analysis standards team, statistical programming, and statistics. This paper will walk through the evolution of the collaboration of the three areas, share success stories of their efforts and consider the challenges facing them in the future | adam dataset, clinical trial, cdisc | source poster source |
5 |
| 4 | A Comparison of Machine Learning Algorithms for Binary Classification of Free-text Fields Using SAS, R, and Python | Michael S Rimler | Clinical data collected via free-text fields are often difficult to use in analysis due to spelling or grammatical errors on data entry by humans, or simply stemming from differences in languages across global sites. Monitoring the data and querying sites to clean the content to support analyses is time consuming, even if the site is responsive and agreeable. Downstream processing of these data in preparation for analyses supporting clinical study reporting can also be quite challenging. This paper compares various methodologies for developing a binary classifier on free-text data. Machine learning methods in SAS, R, and Python are compared to an exact string search in SAS developed for 100% accuracy on the training dataset. A comparison of results of applying the trained models on the test dataset is presented, both individually and as combined methodologies | logistic regression, proc hpforest, training set, logistic distribution, regression analysis, data cleaning, feature engineering, type ii error, type i error, machine learning, adam dataset, unstructured data, fullstimer, proc hpsplit, proc freq, data mining, proc step | source presentation source |
12 |
| 5 | A Dynamic-automated Approach to Reviewing RTF Files | Hrideep Antony, Youying Yu |
This paper introduces "reviewfiles" utility which automates the review process of multiple RTF files. Combined features of SAS language, web scripting languages, and SAS Enterprise Guide (SAS EG) project functionality are used to build this utility. The current version offers a verity of file review capabilities such as summarizing the differences in the RTF files based on any changes to the contents, providing a refined version of changes to the contents based on a threshold value (eg. Search for changes to p-values > .5, etc.), creating a search result on all the files in the folder based on a key-value (eg. Search for file names and summary statistics relevant to a specific preferred term "Dizziness" etc. ), Extracting the first title of the RTF files and providing hyperlinks to the files for easy single-click access to the files. This utility not only makes the review of files easier but will also act as a means to control the quality of the overall deliverables | sas enterprise guide, macro definition, sas enterprise guide project, sample, ods xml, dataset | source presentation source |
10 |
| 6 | A Journey Through End to End Data Flow | Marcin Makowski, Stayce Murray |
Many organizations pursue digitization and automatization of the data flow in the clinical trial process. The creation of a platform that would encompass the whole end-to-end process from study concept to Tables, Figures, and Listings is a challenging one. UCB - a mid-size biopharma company has embarked on just that journey. Building a platform with an initial focus on risk management and protocol complexity assessments, the system's core functionalities hold the foundation to a digital protocol including the schedule of assessments, and protocol risk information. In further stages, the system was expanded to include functionalities around Quality Tolerance Limits monitoring and vendor risk management. Through this functional and data growth, a repository of digitized protocol information was created. This foundation and growth have empowered further development that focuses on digital data flow | clinical trial, machine learning, clinical research, crf, edc, artificial intelligence | source presentation source |
12 |
| 7 | A Programmer's Journey through the BIMO Submission Process | Raghavender Ranga | In February 2018, the Food and Drug Administration (FDA) published draft guidance on the standardized format for Bioresearch Monitoring (BIMO) documentation in a New Drug Application (NDA). This paper will share the authors' experience in developing a comprehensive BIMO package for a recent NDA submission. It will discuss a programmatic approach to converting clinical study report (CSR) listings into BIMO subject-level site listings. It will describe some of the challenges in deriving the summary-level clinical site dataset, with respect to summarizing variables like treatment arm, the need for additional population variables, the use of screen failure data not in the clinical database, and subjects in satellite sites | clinical trial, dataset, csr, treatment arm, proc report, clinical database, standard deviation, efficacy summary, txt, crf, proc step, data science, sample | source presentation source |
12 |
| 8 | A Real-world View of Cataracts | Jeffrey Headd, Walter Cedeno |
Cataract, an eye disease characterized by the clouding of the lens, currently affects over 25 million people in US. This number is projected to double by the year 2050 according to the NIH Eye Institute. To further elucidate drivers of cataract progression, we present an analysis of Real- World Data (RWD), which has gained wide acceptance in the pharmaceutical sector as a tool to understand disease progression, patient journey, unmet patient needs, and other areas. In this work, we apply methods in unsupervised machine learning, including K-Means, Mini-Batch K- Means, Agglomerative Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Interactive Clustering, and K-Modes techniques to Electronic Health Records (EHR) to explore the nature of variation within cataract patients | agglomerative clustering, cluster analysis, machine learning, euclidean distance, outlier | source presentation source |
11 |
| 9 | A Visual Tool to Aid Traceability | Jesse M. Pratt | Traceability is a critical component of any clinical research study. This paper discusses the use of interactive flow charts generated in SAS to trace outputs and analysis data sets to their various sources through the course of a study. Chart layouts are designed prior to any programming taking place, followed by the creation of a positioning data set and then generation of the chart itself using the Graph Template Language. The addition of HTML functionality results in a user-friendly, efficient, and interactive way to navigate across data sets and outputs. Illustrative examples highlight each step | data lineage, graph template language | source presentation source |
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| 10 | A Visualization approach for Clinical Trials with Digital Data | Siddharth Kumar Lokineni | With billions of dollars and countless hours of effort spent each year validating new drugs; devices; and medical interventions; it's little surprise that the Pharmaceutical industry is turning to advanced technology to streamline the process. Big Data takes center stage in the current Digital World and is creating opportunities for better healthcare. The amalgamation of consumer data from wearable devices and the digital capabilities of the technology in hand provide numerous opportunities in clinical trial process. The interaction between new sources of digital dat a and best of old methods in clinical trials will unravel new opportunities to researchers. This paper differentiates traditional trials and modern trials approach with digital devices embedded. Further; this paper explores the strategy to integrate structured and unstructured data and integrate this to Big Data framework | clinical trial, data visualization, unstructured data, clinical data, big data | source presentation source |
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| 11 | Accelerating Translational Research by Leveraging AI and Machine Learning | Leveraging AI | The web has revolutionized the conduct of global, cross-disciplinary research. In the life sciences, interdisciplinary approaches to problem solving and collaboration are becoming increasingly important in facilitating knowledge discovery and integration(1). The big data revolution, accompanied by the development and deployment of wearable medical devices and mobile health applications (2), has enabled the biomedical community to apply artificial intelligence (AI) and machine learning (ML) algorithms to vast amounts of data. AI and ML is fundamentally changing healthcare, creating new research opportunities in predictive analytics, precision medicine, virtual diagnosis, patient monitoring, and drug discovery and delivery(3) | machine learning, sample, neural network, artificial intelligence, extract, clinical trial, deep learning, life sciences, sample size | source presentation source |
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| 12 | Accessible Reporting by PROC STREAM | Charumathy Sreeraman, Samundeeswari Raja |
Reporting conformance to ethical and scientific integrity of clinical trials are monitored by various committees. Such monitoring is mainly performed by creating and reviewing the safety reports of a study. Availability of interactive and dynamic representation of the safety data in the form of tables and listings facilitates understanding of the data reported and eases the reviewing/validation process. These reports are commonly shared via email in printable PDF format. The best part of reports in this format is, they are easily printable on many different printers without adjusting margins or worrying about page breaks. PROC STREAM procedure is a powerful tool in a statistical programmer's arsenal because it enables easy generation of reports which clearly stream the, otherwise lumpy safety data. This paper shall discuss about the usage of PROC STREAM procedure, through simple coding for generation of various types of reports in PDF format |
proc step, macro definition, macro loop, macro variable, %if-%then-%else | source presentation source |
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| 13 | ADaM Data in High Definition | Varia Cartledge | where statement, excel, cdisc | source presentation source |
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| 14 | Adapting to Adaptive Clinical Trials | Renu Shukla, Wenmei Ge |
Poster | clinical trial, adaptive design | source | 1 |
| 15 | Adverse Event and Treatment Emergent Approaches in the Industry | Aimee Basile | adverse events, clinical database, serious adverse event, medical history, trial elements, ctcae grade, crf, randomization | source poster source |
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| 16 | All about versioning - an intro into Distributed Version Control | Mark Bynens | Today as software developers, programmers or data scientists we need to work as a team and engage in collaborations to share our work. R, Python and other open-source software are globally used in pharma in various setups. As more open-source code has been developed, there is a huge need to collaborate and secure our code in a global environment. Repository management services have become a crucial part of collaborative software development. They can help software developers, programmers and data scientist organize their coding work, maintain quality, collaborate and share. This paper introduces collaborative software development, versioning, different types of version control systems, repository management services in general and will compare GitHub, Bitbucket, and GitLab | data science, excel | source presentation source |
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| 17 | An Approach to Dynamic Data Visualization with dplyr DT and Shiny | Megan Kendellen, Nicholas Masel |
Timelines to develop regulatory documents are ever shortening, making manual review of large statistical outputs increasingly untenable. Manual review is also error-prone and hinders collaboration with cross-functional subject matter experts to select relevant data for in-text presentation. To address these challenges, we have developed an automated Data Visualization Tool. The tool converts data table outputs in Rich Text Format (.RTF) or .DOCX to a data frame and determines the number of columns within the table . Coupled with dplyr 's _at() variants, this provides a solution to dynamically process any table regardless of the number of columns. A Shiny user interface makes the tool end -user friendly. The result is a flexible t ool used to rapidly identify data points that exceed any desired incidence or are differentially reported across treatment groups at any desired threshold | proc freq, clinical research | source presentation source |
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| 18 | An automated code translation approach in SDTM specifications using %translate macro | Hrideep Antony | This paper presents a standardized language specification writing approach that increases the overall efficiency of the SDTM programming. This SAS macro-based system invokes a %translate SAS macro which will scan through the SDTM specification document and identify all the variables for which the derivation can be translated to a SAS code and process those variables directly. It can be observed that around 50 percent of SDTM variable derivations involve directly reading raw data variables or a simple subset of data from the source. The %translate macro can also read the formats from control terminology and apply the formats to the data when applicable. The capabilities of this utility macro are currently being expanded with the ability to produce categorical and numerical summary statistics based on a one-line script which the users such as statisticians, medical writers, and content writers will be able to use to produce summaries effortlessly, without the need of any programming | macro variable, raw data, macro definition, cdisc | source presentation source |
7 |
| 19 | An Interactive Visualization Application for Patient-reported Outcomes with Spotfire | Matt Buckley | Interactive visualization tools provide researchers with a quick way to answer pertinent research questions and guide further analysis. These tools are especially helpful in the domain of Patient Reported Outcomes. This work demonstrates a TIBCO SpotfireTM application built for this domain. It highlights demographic data, clinically meaningful changes, questionnaire completion rates, and assessment measures by visit. It is also highly customizable, allowing users to filter for the population of interest and segment analyses by stratification variables. However, for large datasets and detailed analyses, Spotfire can become unresponsive and slow. The application detailed in this work was optimized for efficient calculations and low storage costs by pre-processing data in SAS. This makes the application responsive and quickly configurable | clinical trial, data visualization | source presentation source |
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| 20 | An Overview of the OCS Core DataFitness Assessment Service | David Jacobs, Janie Ma |
data cleaning, adverse events | source poster source |
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| 21 | An Update on CDISC-SEND Data Submissions to FDA | Jesse Anderson, Christopher Bartlett, Jennifer Feldmann, Valerie Gross, Alejandro Mankin, Qingying Lu, Kathryn Matto, Lilliam Rosario |
Over the past year, the US FDA received many electronic data submissions in CDISC-SEND format for nonclinical toxicology studies in new drug applications. Standardized data is used to quickly deliver key findings from nonclinical studies to pharmacology reviewers and aid in the efficiency of the review process. The FDA's KickStart team reviews a number of these CDISC-SEND submissions and reports on data quality issues identified during their review. The number and type of data quality issues identified in SEND submissions have changed over time, mirroring the success in industry-wide adoption of the SEND standard. If the industry addresses the most-frequently seen issues that may impact review, reviewers will be able to maximize the use of data submitted to the FDA for review | data cleaning, sample, cdisc | source presentation source |
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| 22 | Analysis Studio: A Tool Framework for Supporting Regulatory Review | Andrea Arbelaez, Peter Glass, Derek Ahneman |
Regulatory reviewers in the Center for Drug Evaluation and Research (CDER) are responsible for determining the safety and efficacy of drug products before marketing in the United States. In the course of regulatory review, safety reviewers generate tables and visualizations for regulatory review documents. OCS Analysis Studio is a web-based application comprised of multiple interactive tools for the creation of tabular and graphical analyses ready to incorporate into these documents. Users are empowered to upload their own clinical trial data, customize the inputs according to their needs, and view the outputs, which change in real time according to user-provided criteria. A simple, intuitive, and consistent user interface across tools makes the regulatory review process more dynamic and decreases training time for safety reviewers. Outputs can be exported and saved as Word documents or JSON templates, allowing users to easily copy and paste analyses | shift table, audit trail, adverse events, treatment arm | source presentation source |
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| 23 | Are There Any Better Options than SAS for TFLs? Yes, There R! | Niccolo Bassani | There's never been such a flow of interest towards the use of R in the pharmaceutical industry as in the past few years, although its practical use still seems to be hindered by several factors, sometimes due to misunderstandings (e.g. t he validation part) but also because of a lack of knowledge of its capabilities. Despite these bottlenecks, though, R is doubtlessly creating its own (larger by the day) niche in the industry. In this paper we will see how R can be used to create TLFs much like the current c ombination of PROC REPORT/PROC TABULATE and the ODS currently does, thus showing its power and capability to play an important role in our industry in the years to come, not as a replacement for, but rather as an alternative option to SAS |
proc step, proc report, proc means, pharmaceutical industry, proc sql | source presentation source |
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| 24 | Are we there yet? Evaluating new non-EDC data sources for clinical trial submission feasibility | Vijay Pasapula | The current possibility that unlimited non-EDC sources of data (Wearables; Electronic Health Records; Smartphones; Custom Apps) will be available for collecting clinical trial data challenges the traditional methods of managing data for clinical trials. This paper discusses the work of the Data Engineering Project within the PHUSE Working Group; Educating for the Future; with a view to determine how best these data can be standardized and utilized for Clinical Submissions. This paper discusses how mobile technology data sources can be integrated (with the existing database), standardized and contribute to analyses that can then be utilized in submissions considering privacy and governance. Progress of the project will be presented during the conference with a chance to receive input from the audi ence | clinical trial, data governance | source presentation source |
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| 25 | Artificial Intelligence (AI)-enhanced Precision Medicine Drug Development | Mohammad Afshar | association rules, clinical trial, artificial intelligence, clinical research, machine learning, correlation, model explainability | source | 9 | |
| 26 | Assessing Non-inferiority and Equivalence from Survival Data | Chintan Arora, Parvee Kumar |
Non-inferiority or equivalence trials aim to show that the test drug is either not clinically worse than the reference (non-inferiority) or is clinically similar to the reference drug (equivalence) with margin calculation involved with both types of trials. There is ample literature available to asses non-inferiority or equivalence for continuous or categorical types of data. There are also copious options available in pre-defined procedures in statistical software like SAS and R; however, there is little written about options available in these tools to assess non-inferiority and equivalence specifically for survival data or calculating sample size for this type of study design. SAS has provided options to evaluate non-inferiority through PROC PHREG by calculating hazard ratio and corresponding CIs, but there are no options to evaluate non-inferiority if LOG-RANK TEST needs to be performed |
sample size, sample, hazard ratio, time-to-event endpoint | source presentation source |
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| 27 | Assessment of Machine Learning Methods in Coding of Concomitant Medications in Clinical Trials | Sanna Herrgård, Carla Gil, Ingrid Holst, John Holmer Nielsen |
In this talk we will discuss automation of coding of concomitant medications in clinical trials using machine learning. We have assessed performance of WHODrug Koda, developed by Uppsala Monitoring Centre (UMC), with data from 12 different studies across different therapeutic areas. We conclude that Koda would enable Novo Nordisk to increase automation level in the concomitant medication coding process from the current 62% to 79% when coding to Drug Code on the Trade Name level. Out of the 21% of the data that did not have a high certainty Drug Code prediction from Koda, 15% did have a single or multiple lower certainty Drug Code predictions which may provide additional value in the coding process. We observe that high certainty Drug Codes and ATC (Anatomical Therapeutic Chemical) codes predicted by Koda have a high conformance with Novo Nordisk coding with 96% and 94% agreement respectively | concomitant medications, machine learning, clinical trial, verbatim term | source presentation source |
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| 28 | Automated Units Verification, Units Conversion and Converted Results Verification Through the Unified Code for Units of Measure and LOINC | Sam Tomioka | lookup, error message | source presentation source |
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| 29 | Automating Module 5 Packaging with Customizable Metadata | Dhananjay Chhatre, Kevin Miller |
In today's regulatory environment, study data packages included in regulatory submissions are being thoroughly reviewed and assessed for adherence to CDISC data standards requirements, and future audits focused on dataset and submission package development are the next logical focus area for inspectors. To mitigate audit risks and improve efficiencies and quality through automation, Gilead has embarked on a journey to develop a suite of utilities to automate Module 5 packaging while implementing Biometrics and Regulatory Affairs/Operations checks using customizable defaulted metadata as the starting point. This paper will focus on the strategic process and flow for how utilities work together, enabling automation and improving efficiencies while allowing for study statistical programmer customization for their specific study | regulatory submission, sdtm domain, cdisc | source presentation source |
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| 30 | Automating the Analysis Data Reviewer's Guide (ADRG) in Early-stage Development Studies | Jing Su, Pranab K. Mitra |
adam dataset, data standard, cdisc | source | 5 | |
| 31 | Automation of Quality Checks for Ongoing CSR Data | Lavanya Peddibhotla, Savithri Jajam |
proc compare, proc step, order by, create table, proc sql, data cleaning | source presentation source |
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| 32 | Automation of Submission Packages Quality Checks | Sendil K. Ramabadran, Anne-Sophie Bekx |
This paper introduces the automation tool for quality & compliance checking of clinical trial data submission packages to regulatory agencies. Up until now, submission packages were manually checked in the form of QC checklist for correctness, completeness and compliance to regulatory guidelines. This can be laborious and tedious job, with the possibility of human error. This automated tool acts as final gateway check or vetting tool before sending to regulatory/publishing group to build the eCTD. This also automates tedious work like checking all hyperlinks in PDF and XML files. By automating the checking and delivery of submission packages, handshaking between regulatory/publishing and data management/stat programming departments is also clear without any confusion across different therapeutic areas, functions (DM, STAT) and packages (SDTM, ADaM etc.) | proof of concept, data cleaning, cdisc | source poster source |
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| 33 | Be the Master for a Master Protocol Study | Ke Xiao | As a new paradigm for clinical research, master protocol studies, either basket, umbrella, or platform trials, demonstrate increasing benefits and reveal promise especially in oncology drug development. The beauty of designing a master protocol study does not only lie in the fact that it can support the evaluation of multiple compounds with/without common controlled arms in different tumor types simultaneously, but also it provides the possibilities that allow patients to move from one investigational drug to another if deem appropriate. On the other hand, given these appealing features, it is also important to realize that compared with traditional clinical trials, to conduct such attractive master protocol studies, there are quite a few challenges on data collection, result presentation as well as analysis | adam dataset, clinical trial, randomization, clinical research, cdisc | source poster source |
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| 34 | Best Practice for Interactive Analysis for Decision-making: An update from PHUSE Data Visualization Project | Xiangyun Wang, Zachary Skrivanek |
The goal of the best practice for interactive analysis PhUSE project is to evaluate how interactive visualization tools can be used as part of regulatory review process. How to balance the best of self-exploratory analysis with the rigor of regulatory requirements? What is the feasible framework to share interactive tools with the FDA? Is there a need to provide an industry-wide common user interface for regulatory reviewers to easily digest data from different organizations? How to address those and many other challenges facing interactive analyses in a regulatory setting is what we aim to explore. We will provide an update on regulatory and business considerations for different approaches and share our learning from an html interactive display pilot and Docker R-Shiny pilot with FDA. Next steps will be covered. We invite everyone to collectively explore the best way to leverage emerging technologies to advance how we consume and share data | data visualization, proof of concept, clinical research, volcano plot, data cleaning | source | 11 |
| 35 | Best Practices for Annotated CRFs | Amy Garrett | sample, data cleaning | source presentation source |
5 | |
| 36 | Best Practices for Building and Testing Interactive Web Applications in R with Shiny | Kelly O'Briant | This talk will feature a gentle introduction to interactive web application development in the R programming language with two open source package libraries:shiny and shinytest. Both packages are used widely in the data science community and are actively maintained by engineering teams at RStudio, PBC. Shiny is an R package that makes it easy to build interactive web apps straight from R, without prerequisite knowledge of web development skills such as HTML, javascript and CSS. Shiny applications are particularly useful for developing dashboards and performing exploratory data analysis through interactive filtering and visualization. The shinytest package provides tools for creating and running automated tests on Shiny applications and the underlying code used to create them. Testing code is a general best practice for all forms of development, and Shiny applications are not an exception | data science, exploratory data analysis | source | 5 |
| 37 | Big Data Analytics with JuliaDB | Vitali Marenny | JuliaDB is an analytical database package, part of the open source Julia language ecosystem, designed for distributed out-of-core processing of data files and statistical processing. Using the Julia programming language and JuliaDB package, data scientists can leverage a single integrated programming language from prototype to production. JuliaDB provides a cohesive workflow for reading, processing and performing data analytics tasks on both small and large datasets. JuliaDB integrates well with other Julia packages such as OnlineStats and Distributed to retain the benefits of an easy to use high-level language with the performance of low-level language | sample, data science | source presentation source |
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| 38 | Big Lab Data at the Fingertips of the Statistical Reviewers Using R Shiny | Mukul K Mittal, Sarah Cannon |
Laboratory data (LB) is one of the largest datasets in oncology clinical trials. Numerous parameters, tests along with multip le time points make analysis and visualization time consuming and cumbersome for both programmers and statisticians. To address these issues, we utilized the power of R statistical software to analyze the data and R Shiny package to make it a visual pleasure. Using interactive dashboard we can subset data as per parameter, test or the time range and visualize them for different cohorts/treatments and flags. This would give us flexibility to visualize data comprehensively or at patient level. These well-designed interactive graphs not only bring clarity to statistical results, they also save a lot of time for reviewers. In summary, R Shiny provides an elegant and powerful framework to turn complicated and large data analysis into interactive graphs at your fingertips | laboratory data, clinical trial | source presentation source |
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| 39 | Building a Deeper Bench: Writing ADaM Specifications | Kristen Reece Harrington, Shane Rosanbalm |
In a world of silos, an organization can get by with having only a few individuals possessing the knowledge to write CDISC-compliant ADaM specifications. While silos do allow folks to become really good at one thing, it doesn't necessarily lead to a better quality study overall. A better model is to have the same programmers responsible for not just one aspect of a study (SDTM or ADaM or TLFs) but rather have them be responsible all aspects of a study (SDTM and ADaM and TLFs). This paper will focus on Rho's efforts over time to expand ADaM spec writing expertise from the core group of ADaM early-adopters to the broader programming group as a whole | adam dataset, data standard, cdisc | source presentation source |
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| 40 | Building Your Central Monitoring Team: Players, Skills, Structure and Communication | Christina Dinger, Lynn Demings |
To embrace the paradigm shift towards Central Monitoring, organizations must develop a structure that meets the needs of clinical trials operating today, but also supports the evolution and expansion of Central Monitoring as the primary means for monitoring data. This paper will outline key components necessary for organizations to create a foundation that facilitates the incremental growth and expansion of Central Monitoring. It will discuss the cross-pollination and integration of roles needed to assemble a nimble project team ready to meet the demands of a complex digital trial. Lastly, it will provide a scalable and cost-effective Central Monitoring model that harnesses technology and data science. Central Monitoring Drivers: Since 2011 organizations have reacted to risk-based regulatory guidance and implemented changes in how they operationalize clinical trials | source presentation source |
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| 41 | Case Study: Integrated SDTM to Integrated ADaM to TLFs | Kishore Pothuri | The Integrated Summary of Safety (ISS) and Integrated Summary of Efficacy (ISE) are essential components of a successful submission. It can be very challenging and tedious to pool data for analysis from multiple studies without losing traceability, since similar information could have been mapped differently in different studies through diverse systems by various vendors, thereby putting compliance and harmonization at risk. What complicates the task further is that the number of studies involved can range anywhere from 2 to 50, or even more. There are multiple approaches to pooling and integrating study datasets for ISS and ISE analyses, namely ADaM Integration using study-level SDTM domains, ADaM Integration using Pooled SDTM datasets, ADaM Integration using study-level ADaM datasets | sdtm domain, adam dataset, sdtm programming, cdisc | source presentation source |
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| 42 | CDISC 360: Using Biomedical Concept Metadata to Generate Case Report Forms and Dataset Definitions | Samuel Hume | This paper describes new metadata created by CDISC 360 and shows how to apply it toward the automated generation of basic study artifacts, such as ODM-based case report forms and a Define-XML specification. Specifically, it explains how an object-oriented Python program processes metadata from four sources to produce a Define-XML file. The four sources of metadata include: (1) Implementation Guide standards, (2) Controlled Terminology, (3) Biomedical Concepts, and (4) Templates. Biomedical Concepts and Templates represent new sources of metadata created by the CDISC 360 project. This paper uses visual representations of the metadata and logic to communicate the technical details. It also describes the role of the CDISC Library in providing developers access to this new metadata via its API. As an ongoing project, readers can influence these metadata constructs prior to final publication | clinical research, crf, cdisc | source presentation source |
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| 43 | CDISC Implementation Primer | Bess LeRoy, Rebecca Baker |
sdtmig, pharmaceutical industry, cdisc | source | 5 | |
| 44 | CDISC Library Try-out: From Implementation to Evaluation of the API | Roman Radelicki | CDISC Library promises linked data and a REST API to deliver CDISC standards metadata for software applications to automate standards-based processes. With my background as a programmer, my curiosity was immediately triggered during the presentation on the CDISC 360 project: a REST API (Application Programming Interface), linked metadata and biomedical concepts! In this paper, I want to share the journey of the CDISC Library implementation in one of our in-house developed tools. To test and demonstrate the implementation of the CDISC Library, we used our TS (Trial Summary Information) dataset creation tool. This tool was the ideal case study candidate because it is a standalone tool which makes use of the CDISC SDTMIG and CDISC CT data, without a major impact on other tools allowing quick deployment of the CDISC Library. The TS dataset creation tool enables the Clinical Data Manager to create the TS dataset for a trial, necessary for submission to the authorities | controlled terminology, trial summary, sdtmig, rest api, excel, cdisc controlled terminology, cdisc | source presentation source |
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| 45 | CDISC's ADTTE Domain: Time-to-Event Analysis From a Programmer's Point of View | Frieder Wolff, Vitali Gering |
survival analysis, clinical trial, overall survival, survival function, censoring, kaplan-meier estimator, bds, cdisc | source poster source |
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| 46 | Challenges and Solutions While Mapping Laboratory Data | Ratheesh Gunda | Poster: In Oncology studies it is quite common to get complex laboratory data from different labs with different standards and measurements within similar tests. There can be variations in normal ranges, collected units, and connotations for collected results. Given the length of a typical Oncology study it is can also be challenging to keep up the changes in standards like NCI, CDISC etc. Lab domain is one of the main safety data sets and Oncology studies contain a myriad of different types of data when compared to a regular study which primarily contains only Hematology; Chemistry; Pregnancy data. Also, each patient will have numerous tests per visit per time point. When not planned properly we will face a lot of confusion when trying to map this data into analysis domains and eventually output into TLFs | missing value, laboratory data, cdisc | source | 1 |
| 47 | Clarity Stack Clinical Technology - A Holistic View of Clinical Trial Operational Status and Patient Safety Data | Dirk Spruck, Lisa Jamba |
Presentation | data warehouse, clinical research | source | 10 |
| 48 | Code Generator to Check Truncated Values in Derived Datasets | Proma Mukherjee | An important part of dataset programming validation involves handling of SAS log messages like overwritten variables, missing values, etc. Since there is no specific log message for character variable truncations, the usual validation method is manual scanning or spot checks of datasets. The intent of this paper is to provide a robust macro script which can be used in dataset codes to check for possible truncation of character variables. It achieves this by checking the length and value of character variab les in the derived and source datasets. The required inputs are the source/derived dataset names which generate 'truncate check datasets'. These macro-generated datasets contain the character variables and their maximum length in a single observation which can be visually glanced through to check for possible truncations | character variable, macro variable, data cleaning, numeric variable | source presentation source |
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| 49 | Common Data Quality Issues Seen During FDA's JumpStart Service | Aaron Belowich, Andrea Arbelaez |
JumpStart is a FDA Center for Drug Evaluation and Research (CDER) service which provides data quality and data analytic support to reviewers. Since 2013, the JumpStart team has assessed the data quality of Study Data Tabulation Model (SDTM) data for over 170 New Drug Applications (NDA) and Biologics License Applications (BLA) using automated tools. The JumpStart data quality assessment reviews the composition and quality of clinical trial data to identify potential issues and errors that may affect a review. As part of the service, the JumpStart team investigates these findings and helps determine their potential impacts for review teams. This paper discusses some of the common findings from the JumpStart investigations that most impacted application reviews. We share these findings to inform applicants of the identified data quality issues to encourage them to submit higher quality data in the future, thereby increasing the efficiency of the review | adverse events, data cleaning, clinical trial, cdisc | source presentation source |
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| 50 | Common Sense Tips for Weaving Governance into an MDR | Kenneth Stoltzfus | The industry is seeing an upsurge of sponsor organizations attempting to unite their standards governance with the technology that houses and manages their standards: a Metadata Repository (MDR). It is a project that varies according to the differences between sponsors regarding architecture, culture, process, and priorities | clinical data, clinical trial | source presentation source |
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| 51 | Complexities in Adhering to Data Standards for ISS NDA Submissions | Vidya Muthukumar | Regulation of new drugs in the US has been based on the New Drug Application (NDA). NDA application is the process by which drug sponsors, such as biotech and pharmaceutical companies, propose that the FDA approve a new drug for commercialization. Drug sponsors use the services of Contract Research Organizations (CROs) to help with the complex NDA submission process. The Integrated Summary of Safety (ISS) is a section of the NDA that provides a complete review of safety information through meta-analysis and pooling of data from clinical trials performed with the study drug. The ISS is useful to, 1) Identify rare and/or unexpected trends in patient subgroups, 2) Improve the precision of results with a larger population size by integrating studies, 3) Reach stronger, more defendable statistical conclusions. This paper demonstrates a recent ISS NDA submission to the FDA and the complexities surrounding the process of adhering to data standards | adverse events, sdtm domain, cdisc | source presentation source |
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| 52 | Confusing Data Validation Rules Explained, Part 2 | Michael Beers, Blue Bell |
The 2018 PhUSE US Connect paper 'Confusing Data Validation Rules Explained' by Pinnacle 21 discussed some of the more confusing data validation rules, including the possible sources of confusion, and clarifications on the intent and purpose of the validation rule. This paper will discuss a new set of confusing validation rules, and explain what the resulting validation issues would mean | data cleaning, data validation rules, trial summary, sdtm domain, concomitant medications, controlled terminology, randomization, cdisc | source | 6 |
| 53 | Connected Monitoring | Mike Henderson | clinical trial, data validation rules, stream processing, real-time processing, proc freq | source presentation source |
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| 54 | Creating a Simplified TS Domain Using the R Shiny App | Hanming Tu | This paper describes how to use R and R shiny design a web-based application to easily generate trial summary (TS) domain dataset to meet the FDA study data conformance requirements | trial summary, clinical trial | source presentation source |
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| 55 | Custom Monitoring Solutions in R: How to Create Powerful Dashboards Using R Markdown | Kelly O'Briant | data science, clinical data | source | 5 | |
| 56 | Data Compliance: Hidden Benefits | Priyanka Adusumilli | The pharmaceutical industry is currently spending a large amount of money updating the CDISC SDTM submission packages according to FDA standards. Once study data is deemed ready for submission by a vendor, most sponsors expect the compliance checks are completed, merely to update the data for that study. However, those same findings from the compliance checks can be used to identify bigger issues with the overall collection strategy. John Powell once said, "The only real mistake is the one from which we learn nothing." Addressing the issues within the individual study for compliance checks solves the immediate problem, until the next study comes along and the same findings are prevalent. Being able to learn from these findings and use them in a positive manner beyond the individual study can help create efficiency and provide a more transparent data transformation strategy that will be in line with the FDA's expectations | raw data, data transformation, cdisc | source | 3 |
| 57 | Data Exchange in Clinical Trial - Programming Perspective | Chao Wang | Data exchange is heavily intertwined in the development and procedures of clinical trials. A large component of data exchange in clinical trials involves programming, and the perspectives of programming professionals. Programmers are responsible for maintaining Clinical Data Interchange Standards Consortium (CDISC), controlling terminology and cleaning related metadata for scientific and clinical use. Challenges that arise during the process of producing, managing and exporting the data include data accuracy and data security, among others. These challenges are made more difficult when working with multi-regional studies, different operating systems, and data obtained from different sources such as the Electronic Data Capture (EDC), Study Data Tabulation Model (SDTM) and Analysis Data Structure (ADaM). These data are finally compiled into clinical reports (tables, listings, and figures) that are then exchanged | clinical trial, raw data, sdtm domain, cdisc | source presentation source |
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| 58 | Data Load Automation | Jennifer H. Desai | Data transfers are essential aspects throughout the duration of a clinical trial. The current Janssen process of manual data transfers with its external vendors is very time consuming. A solution to enhance the manual data transfer process is with data load automation. This paper will describe Janssen's current manual data transfer process, and how data load automation can be implemented to create a more efficient and effective method for data transfers between Janssen and its vendors | clinical trial, raw data, proc freq, life sciences | source presentation source |
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| 59 | Data Review of RECIST Criteria using ML/AI A Proof of Concept Example | Priya Gopal | Machine learning (ML)/Artificial Intelligence (AI) provides the ability to learn from data patterns and make decisions without the need for explicit programming. In oncology clinical studies, data management/medical review team verifies the tumor measurements and corresponding assigned RECIST criteria using manual approach to determine quality of the response data entered by the sites. Depending on the size of study population and complexity, the review process may involve additional resources and time to check for each data point in the study. This paper focuses on a proof-of-concept (POC) of how machine learning (ML) algorithms/Artificial Intelligence (AI) could help increase efficiencies with review and cleaning process of tumor data. Realistic simulated data containing tumor measurements and corresponding RECIST response values are synthesized and used to train the various algorithms and neural networks in identifying the data patterns to infer RECIST criteria | neural network, data cleaning, imputation, missing value, machine learning, artificial intelligence, network optimization, training set | source presentation source |
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| 60 | Data Science Project (Educating for the Future Working Group) | Aldir Medeiros Filho, Sascha Ahrweiler |
According to Google Trends, the word "Data Science" is currently at a peak interest for worldwide searches. Companies like Uber and Amazon have built entire business models using data science methodology. The healthcare sector has also seen new players especially in consumer devices, which increased awareness of healthy lifestyles by applying advanced analytics. The pharmaceutical industry is adapting to these new technologies. FDA approved or cleared devices are already used mainly for exploratory purposes in clinical trials and create huge volumes of data. We can create valuable insights when we connect these new data sources to clinical data and apply Data Science methods like Machine Learning, Deep Learning or Artificial Intelligence. This paper discusses the Data Sciences Project within Educating for the Future Working Group, with a view to educate the broader PHUSE community in data science techniques in healthcare so they can be prepared in dealing with new challenges | clinical trial, data science, clinical research, null hypothesis, sample, clinical data, phase i, confidence interval, hypothesis testing, machine learning, phase iii, primary endpoint, sample size, treatment effect | source presentation source |
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| 61 | Data Standards for eCOA in Clinical Trials | Terek Peterson, Franklin E. Menius |
Adoption of data standards are necessary to underpin higher data quality, efficiencies and integrated applications across increasingly complex clinical research processes. The US Food and Drug Administration (FDA) and Pharmaceuticals and Medical Devices Agency (PMDA) have clearly embraced data standards for the submission of data, laying the groundwork for expanded industry adoption. Collecting data in a standardized way is the next step to reduce messy, time-wasting efforts that can negatively impact patient safety, development timelines and resource utilization. The adoption of data standards for electronic Clinical Outcomes Assessments (eCOA), the fastest growing technology in the eClinical space is critical to ensure quality and efficiency in this rapidly evolving discipline. The paper will discuss the benefits of implementing standards and which forms of data collection standards companies are requesting | data standard, clinical research, clinical trial, data cleaning, controlled terminology, cdisc | source presentation source |
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| 62 | Data Standards Used for Electronic Submission of Real-World Evidence in New Drug Application (NDA) | Nicole Thorne, Kathy Wu |
The current standard of care therapy for the disease under study, is shown to have a low response for subjects with the specific tumor biomarker alterations we are targeting. Using a real world matched clinical and genomic dataset of patients with the disease, we explored the predictive value of this specific tumor biomarker alteration to the current standard of care therapy. At the pre-NDA meeting results of these real-world analyses were presented and decided to be submitted as a white paper, to be included in the submission. At the same meeting, the Federal Drug Administration (FDA) requested that the data to support this analysis be included in CDISC format. This paper covers our experience in submitting the Real-World Evidence (RWE) data in CDISC format, including the ADaM structure we used, other factors impacting our decisions, and our experience with a FDA technical walkthrough | clinical trial, overall survival, raw data, adam dataset, concomitant medications, sdtm domain, cdisc | source presentation source |
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| 63 | Data Visualization for Medical Review | Bharath Sunkavalli | Data Visualization (DV) can be applied effectively to several functional areas in Clinical Research. This paper highlights the applications, techniques and advantages of DV specific to Medical Review function. Medical Experts are the end users of this function who reviews clinical data throughout the duration of study to ensure patient safety and proper study conduct. DV involves creating interactive Subject level and Summary reports of clinical data and then allow end users to review data using several features like Drill down, data highlighting, Subject level filter and many more to get quick insights of data and take prompt actions. Mock Dose Titration data from Cardio Vascular Trial will be used in this paper for demonstration | dose modification, bar chart, clinical research, adverse events | source presentation source |
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| 64 | Data Wrangling - Combining and Restructuring QS-related ADaM Datasets for Pathway Predictive Modeling | Stanley Brill | data science, clinical trial, cdisc | source poster source |
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| 65 | Data, Metadata and the notion of Holons* (* Information concepts) | Peter Tormay | data standard, clinical trial | source presentation source |
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| 66 | Demystifying the Data World of Biobanks with Machine Learning | Bob Engle, Himanshu Pandya |
Since last few decades Biobanks have become an important resource in medical research supporting many types of contemporary research like genomics and personalized medicine. Biobanks give researchers access to data representing a large population. They collect, catalogue and store biological samples and associated clinical data. One such Biobank is the UK Biobank (UKBB). UKBB is one of the 10 largest Biobanks of the world and with around 500K subjects RWD. Its data is currently being used by many researchers (including Biogen) in the life science industry. This paper illustrates overview of the UKBB, the associated data curation process, salient features of the process, challenges faced and how ML is being leveraged to gain invaluable data insights | machine learning, clinical data | source presentation source |
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| 67 | Design and Conduct of Clinical Trials: Past Present and Future. New Innovative Technology Methods | George Obsekov | A technically strong Data Management team defines pre-study uncertainty of intervention effect, soft primary outcomes, and inadequate cross-over/ parallel design of trials as the main failure for older studies. The team described a wonderful world of current statistical analysis for blocked, stratified randomization and extreme strata methods as primary selection criteria for current trial conduct. We describe benefits, drawbacks and biases effects of case-series, case-control, and cohort study design | clinical trial, sample, randomized controlled trial, randomization, primary endpoint, clinical research, secondary endpoint | source presentation source |
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| 68 | Developing SEND for CBER - Collaborative Workstream Initiative Update | Lisa Lin, Susan DeHaven |
The Center for Biologics Evaluation and Research (CBER) within Food and Drug Administration (FDA) intends to receive Standards for Exchange of Nonclinical Data (SEND) datasets in future submissions. However, implementations currently defined in published and planned SEND Implementation Guides had not been evaluated regarding data models needed for representing certain study types (including study design) or endpoints typically received in a CBER submission. The SEND for CBER team was formed to evaluate endpoints related biodistribution, immunogenicity, test article characterization and hybrid activity/safety trial designs with the current SEND domain models available to understand gaps and possible new requirements. To address the intention of CBER, a team has been developed and project work strategy followed to evaluate essential SEND data tabulation needs for supporting CBER submissions | liquidity gap, controlled terminology, data standard, cdisc | source presentation source |
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| 69 | Digitalization of Clinical Study Report (CSR) Tables and Listings for Cross-report Verification | Iraj Mohebalian, Michael Badlani |
Large-scale efficacy clinical trials generate numerous table and listing reports in PDF or MS-Word format for inclusion in Clinical Study Reports. The importance of data accuracy, frequency of generation and sheer length of reports combined, make their verification crucial, time-consuming, and prone to human error. This paper presents the framework and one of the tools we have developed at Bayer to automate a number of verification checks that help improve the quality of our TLF deliverables by performing various consistency checks for every study deliverable. This is done by digitalizing and storing the contents of the reports to a standardized cell-based data repository with each cell stored having multiple properties associated with it. We present specific examples of our tool using mock data from a Phase III clinical trial study and show the benefits such a tool can have in improving the quality of the deliverables | clinical trial, sas log | source presentation source |
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| 70 | Disruption is Personal: RWE and AI Changing the Game for Personalized Medicine | Arnab Goswami | Conventional clinical trial approach deals with data from patients enrolled in a designated trial and represents a small portion of the actual patient population. Real world evidence (RWE) is developed using Real world data (RWD) that reflects use of broader, more heterogeneous populations and utilize variety of sources like clinical data, administrative/claims data, patient-generated/reported data, and emerging data sources including social media. Personalized medicine based on RWE adopts a data-driven approach and aims to design the best treatment option tailored to individual patients, using predictive analytics from a large data set. Artificial Intelligence tools like Neural Network and Deep Learning algorithms can act in tandem to generate more decisive outcomes than the traditional stratified approach, as every patient is different | clinical trial, deep learning, neural network, artificial intelligence | source presentation source |
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| 71 | Documentation, Applicability and Automation: How Best Practices Can Help You Achieve Best Results | Aadesh Shah | How best practices can help you achieve best results, this paper will walk you through briefly on documentation, what to document and how much to document as well as the factors of traceability, validation and accountability. Applicability talks about dependency around clinical programming and how we can secure it manually if no automated checks are available using a Quality Gate process. The last part of this paper talks about Automation, Automated checks can provide the rigidity of double programming with more efficiency, but only if time is invested upfront to develop the tools and processes. It can lower the potential of human error and be applied and executed faster than manual processes | macro definition, data cleaning, adam dataset, proc step, independent qc, cdisc | source presentation source |
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| 72 | Dual Roles: I'm not just a programmer! | Laura Williams, Hunter Vega |
Any successful business will be able to hire and retain great employees. One critical component to retaining those employees is establishing a focus on professional development that originates with the company leadership. Contract research organizations (CROs) are no exception. One unique type of professional development occurring in our organization is the opportunity for dual roles, not only in management but in operational areas. When an employee has reached a senior role in his/her department, they may be interested in ways to transition into roles with more leadership potential and/or a demand for higher-level technical skills. The focus of this presentation will be on the ways statistical programmers can work in dual roles and expand their skill set, as well as support different operational groups and become involved at the onset of project planning | statistical programmer, clinical programming, data science, clinical trial | source presentation source |
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| 73 | Early Detection and Prevention of Compliance Issues | Vara Prasad Reddy Sakampally | Creating the datasets that meets all compliance requirements is a challenging task and most companies do check for compliance and quality at the end of study. Identifying the issues at the later stage involves additional resources and risks the deliverable timelines as the issues are to be addressed. By adding a process in place that will check for compliance at the stages of specification creation and dataset development will minimize the quality issues at final delivery. This paper will propose various approaches and how to call the pinnacle application (which is considered as the gold standard in the industry) through a SAS session to assess the quality of dataset during its development phase. By adopting such a process; majority of compliance issues can be addressed before passing to the validation | controlled terminology, data cleaning, proc freq, cdisc | source | 5 |
| 74 | Educating for the Future: Data Engineering Techniques | Mohit Juneja, Renu Shukla |
source poster source |
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| 75 | Efficacy ADaMs in Oncology - Step by Step (Dataset by Dataset) | Ilya Krivelevich, Ran Xie |
The CDISC ADaM team has designed the Analysis Data Model (ADaM) standards to support submission to regulatory agencies such as the U.S. Food and Drug Administration (FDA) and Japan's Pharmaceuticals and Medical Devices Agency (PMDA). Pharmaceutical companies are required to follow the ADaM st andards for analysis datasets to ensure a successful submission. The design of analysis datasets is generally driven by the scientific and medical objectives of the clinical tria l. In oncology, efficacy endpoints can be classified as 'w ith' or 'w ithout' censoring. ADaM guidance recommends storing analysis results for censoring parameters and non -censoring parameters separately; therefore, there is a need to have tw o ADaM datasets w ith derived subject-level parameters. This article sheds light on creating analysis datasets for oncology efficacy (specifically for solid tumors) w ith step-by-step techniques and examples. The proposed methodology can be used for various evaluation criteria | adam dataset, sensitivity analysis, clinical trial, sdtm domain, standard deviation, data science, duration of response, survival analysis, cdisc | source presentation source |
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| 76 | Employee Branding: Cherished Through Connect | Supriya Moghe | source poster source |
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| 77 | End-to-End Interactive TLF Using R Shiny | Rohit Banga | This paper will demonstrate how to design an end to end web application to make interactive statistical outputs - Tables, Listings and Figures (TLF) using R Shiny Dashboard. The paper will describe in detail how SAS XPT files from different studies can be loaded in R. The read data will then be used to create interactive statistical reports - summary tables, graphs, and data listings at real time using. The paper will then describe how these reports can be downloaded in different formats such as CSV, PDF and RTF etc. Finally, the paper will detail how the shiny app can be deployed on web and made available to end users. This method has great application in creating data science and data monitoring applications that can optimize data management tasks and statistical analysis workflow. Statistical reports and interactive dashboards can be created and made available in real-time, using open source tools, and without waiting for programmers to create reports |
adverse events, adam dataset, data science | source presentation source |
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| 78 | eSource Arising: Clinical Data Automation Possibilities Beyond Data Capture | Sofie Stynen, Annelies Van Zeveren |
When implementing the 1st generation of eSource system at SGS, the focus was on facilitating data capture and converting clinical data directly to SDTM, making eCRF redundant. After getting familiar with eSource, we are now ready to use our gained knowledge and expertise and take eSource clinical automation to the next level with the 2nd generation of eSource system. This paper will highlight our acquired insights and challenges faced from both systems resulting in improved efficiencies. Focus on standardized data and the use of code lists to convert easily to SDTM are key for efficiencies. With a proper test environment set up in the 2nd eSource system, it is now possible for data management to enter dummy data, making it easier to set up SDTM conversion. Data visualization and accessibility have improved significantly, and the new query management seems to allow for shorter data cleaning-timelines | sample, clinical data, clinical trial, data governance, laboratory data, adverse events, cdisc | source presentation source |
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| 79 | Evolution of Clinical Data Disclosure and Transparency | Sneha Paradkar | The pharmaceutical industry has put greater focus on posting results of clinical trials for public disclosure. Data transparency is an important aspect of clinical research as it helps in enabling evidence-based decisions in medicine. Clinical data disclosure and transparency is of utmost importance due to legal obligations and compliance factors associated with data disclosure. If sponsors or research organizations do not disclose clinical trial results on time or fail to post results at all, it may lead to misconduct or data fraud in clinical trials, also posing legal penalties to the organization. Publishing results from clinical trials helps in reducing the global costs of drug development. In the past few years, there has been a significant change in the way clinical trial results are posted | clinical trial, clinical data, pharmaceutical industry, clinical research | source presentation source |
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| 80 | Evolution of Data: From extraction to visualization | Ashish Kumar | data review, clinical trial, data cleaning, clinical data, data science | source presentation source |
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| 81 | FDA and PMDA Study Data Submission Distinctions | Kiran Kumar Kundarapu, Nancy Bauer |
The Food and Drug Administration (FDA) and the Pharmaceuticals and Medical Devices Agency (PMDA) require standardized study data for certain regulatory submissions . Industry approaches to meeting these requirements vary across sponsors and between regulatory review divisions. Within the PHUSE 'Optimizing the Use of Data Standards' working group, the 'Industry Experiences Submitting Standardized Study Data to Regulatory Authorities ' sub team established a project to recommend best submission strategies and planning practices. In the end-to-end process of drug development, it is essential to be aware of agency distinctions between the respective regulatory submission requirements. This poster addresses the similarities and differences in standards between the FDA and the PMDA covering mandates, data packages, dictionaries, validation rules, meetings and timelines. Additionally, questions to help sponsors plan for submitting to both agencies based on industry experiences are included | data cleaning, clinical research | source poster source |
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| 82 | FDA Study Data Guidance Update | Ethan Chen | Study Data Standards listed in the Food and Drug Administration (FDA) Data Standards Catalog are required for clinical and non-clinical studies that started after December 17, 2016 for Abbreviated New Drug Application (ANDA), New Drug Application (NDA) and Biologics License Application (BLA) or December 17, 2017 for Commercial Investigational New Drug (IND). Through the technical rejection process, FDA can reject an application because of its technical deficiencies, based on the severity of the Electronic common technical document (eCTD) validation criteria which are specified in the "Technical Rejection Criteria for Study Data" (TRC). FDA conducts quarterly analyses on submissions containing study data received by the Agency to assess conformance rates to TRC. Based on findings from recent analyses and feedback provided by industry, FDA made three important clarifications to the TRC in October 2019 | clinical trial, data cleaning, data validation rules | source presentation source |
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| 83 | FDA View: FDA Study Data | Heather Crandall | clinical trial, data cleaning, data validation rules | source | 6 | |
| 84 | Feature of SDTM Mapping and Conversion Using NLP | Eric Weising, Shiva Katepalli |
SDTM mapping process is redundant and time consuming, which always required a CDISC-SDTM Analyst intervention while creating the mapping specification in order to meet regulatory requirement. Automating the mapping process by utilizing emerging technology such as Machine Learning will effectively reduce the manual efforts and increase the quality of conversion study by study. System which uses Natural Language Processing (NLP) logics with continuous learning, eventually will help to predict not only the standard domain mapping but also will learn the creation of custom domains and will perform those mappings automatically in similar situations. Outcome of using this emerging AI technology, is gearing up the standards mapping and encouraging the effective use of SME's time. This paper will discuss one such algorithm of Machine Learning which was implemented as a tool | sdtm programming, machine learning, sdtm domain, cdisc | source presentation source |
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| 85 | Feedback to Feed Back | Ankur Mathur, Harper Forbes |
statistical programmer, clinical programming, data science, clinical trial, clinical data | source presentation source |
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| 86 | Fill Patterns to the Oncology Plots Using SAS | Dharmendra Tirumalasetti, Vikram Chanduri |
In Oncology trials, it is very common to produce the plots with color coding based on grouping variables like Disease Responses using PROC SGPLOT. However, in some trials, there will be a requirement to use both fill patterns and color to distinguish between multiple group variables. Fill patterns can be applied to any plot using an option FILLPATTERN in SAS v9.4M5 or later releases. However, there is no such option available in the previous versions of SAS. In this paper, we will present how one can generate the fill patterns and colors to the plots that are widely used in Oncology trials (e.g. Waterfall plots) using the power of PROC TEMPLATE and PROC SGPLOT in SAS versions before 9.4M5. We will also provide some examples of the plots that will help the end user in reviewing the |
waterfall chart, proc step, proc sgplot, disease response, proc template, class variable | source presentation source |
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| 87 | Generating TFLs in R - Challenges and Successes Compared to SAS | Amol Wayker, Andrew Miskell |
Companies have been creating TFLs using SAS for decades. More recently, R usage has increased for TFLs. As this trend continues, knowing R is only a first step. There are many challenges both in coding and infrastructure which must be foreseen to replicate the results produced by SAS. We will present examples of issues which arose as our department started using R for some TFLs. Examples include sorting and rounding differences, derivation of p-values and LS Means, producing submission quality reports, and handling version updates. Some resolutions are based on knowing the different defaults SAS uses rather than R. Others are based on creating R functions to work around different assumptions that R and SAS make. Still others depend on users being aware of the different ways the languages work. We will present one use-case where we still have not figured out how to replicate the values in R | proc step, proc freq, fisher exact test, missing value, exact test, character variable, numeric variable, proc report, proc sort, box plot | source presentation source |
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| 88 | Get in Your Lane! Building Swimmer Plots Using SAS 9.4 | Kristi Garner, Sri Talluri |
Poster: Swimmer plots provide the capability to quickly and intuitively understand the clinical progression of a subject during an oncology trial, as well as visually comparing individual subjects on various regimens. A variety of time to response variables can be plotted to give the viewer an easy-to-understand graphical overview of a subject's timeline, including tumor status milestones, duration of response, and prior/concomitant therapies. In addition, significant other occurrences such as adverse events of special interest can be identified and temporalized. Unlike other types of plots, a swimmer plot can also indicate the subject's eventual disposition or survival status. New PROC SGPLOT and SGRENDER options for SAS 9.4 are available to further identify and highlight specified events. This poster will illustrate how these new options can be used for maximum visual effectiveness |
swimmer plot, procedures | source | 1 |
| 89 | Get to the Bottom of Lab Toxicity Grading: Challenges and Implementation of CTCAE Version 5 | Xiaoyin Zhong | The NCI Common Terminology Criteria for Adverse Events (NCI-CTCAE) is a set of criteria to assign laboratory toxicity grades in Oncology trials. A laboratory toxicity grading is an important part of safety reporting, therefore it is critical to stay up to date with various CTCAE versions. Compared with CTCAE version 4.03, the latest CTCAE version 5.0 contains 54 new/updated algorithms in 19 lab parameters. Additionally, it adds a layer of complexity with grading criteria dependent on baseline measurements. In this paper, we will present the approach used for deriving the toxicity grade based on CTCAE version 5.0, as well as challenges we encountered | ctcae grade, reference data, laboratory data, clinical trial | source presentation source |
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| 90 | Getting e-Closer to Multi-Regional Clinical Trials | Lily Ling, Yuan Yuan Dong, Yanhong Li |
With the increasing globalization of drug development, it has become more important and popular that data from multi-regional clinical trials (MRCTs) can be accepted by regulatory authorities across regions and countries. The multi-regional data often serves as the primary source of evidence to support marketing approval of drugs. This paper details some experience of different MRCTs filing needs across regions and countries, and different database lock (DBL) designs for MRCTs. Additionally, we will share some technical tips that make our programming more efficiently in order to better serve MRCTs | clinical trial, subgroup analysis, adverse events, analysis set, clinical research, phase iii, clinical data | source presentation source |
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| 91 | Getting It Right: Refinement of SEND Validation Rules | Kristin Kelly, Blue Bell |
The CDISC SDTM metadata, outlined in the SDTM Model, are used for submission of data from both clinical trials and nonclinical studies. Until recently, many of the Pinnacle 21 validation rules were assigned for both SDTM and SEND domains when in some cases, a specific rule did not apply for SEND data as outlined in the SENDIG. Over the past year, the SEND rule set ha s been refined through the modification of existing rules, removal of others and creation of new rules. All rules are based on either an FDA Business rule, an FDA Validator rule, or CDISC rules. This paper will discuss some of the changes that have been m ade in an effort to 'get the rules right' for SEND | data cleaning, data validation rules, type i error, cdisc | source presentation source |
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| 92 | Giving Back to the Community - Managing CDISC Terminology | Dorte Straarup | cdisc controlled terminology, controlled terminology, cdisc | source poster source |
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| 93 | Good Cooperation Between SAS and R for Regulatory Submission | Jingyuan Chen | Recently there has been a trend for using R in pharmaceutical industry, though SAS has been a dominant software over the years. In this paper, I am going to illustrate how SAS and R can complement each other for creating health authority submission package. R was involved in preparing for eSUB Module 5 in one of the submission I worked on before. This approved that R can make the products with submission ready quality. Additionally, making use of the advantages from each tool can provide an excellent work for Health Authority's requests. Recently, I was involved in an EMA request for exploratory analysis, where SAS was used to implement the part of step-wise modelling selection and R markdown was used for the rest part of exploratory analysis. Therefore, detecting the right tool for right situation can provide us the good quality, clear documentation and efficient manner for submission | regression analysis, forest plot, feature selection, clinical trial, pharmaceutical industry, data science | source presentation source |
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| 94 | Graphical Presentation of Patient Profiles in Dose Escalation Trials Using SAS GTL | Chiu Tsang, Rita Tsang |
Poster | graph template language, dose escalation | source | 1 |
| 95 | Great!! Not So Great ... How to Capture it All in an Effective Lessons Learned Discussion | Monali Khanna | Each day, while executing tasks during various phases of projects, techniques for improvements are discovered. Every lesson learned from a project can be valuable for future projects. The key to success is conducting lessons learned meetings to record project successes and areas of improvements and sharing these across teams to take advantage of organizational best practices and prevent teams from repeating past mistakes. Having had the opportunity to facilitate lessons learned sessions for both highly successful projects and projects with multiple challenges, I have gained valuable firsthand experience in discussing both scenarios. This paper will cover topics around conducting an effective lesson learned meeting from these experiences. The paper will outline preparations for a productive lessons learned meeting, how to achieve maximum participation during the meeting, and course of actions to be taken after the meeting | randomization, extract | source presentation source |
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| 96 | Handling Duplicate Records in SDTM for Effective Resolution and Explanation in Data Submission | Varun Debbeti | One of the challenges of SAS programmers is handling duplicate records in the raw/SDTM data. These are observed due to unclean data or programming inefficiency or due to the necessity to collect multiple results for special analytical or statistical purpose in the trial. In some cases, these are much harder to manage and can complicate analysis by producing unexpected outcomes and programming delays. This paper presents the background to identify different types of duplicate records by using the concept of Natural Keys and Surrogate Keys described in the SDTM IG 3.2. It also discusses FDA business rules, FDA Validator rules, corresponding Pinnacle21 (short form P21) Validator rules and warnings in P21 report that covers the duplicate records. The background is used to classify the most common types of duplicate records in to 5 categories. This paper then provides examples and prescribes standard set of actions to resolve the issue in each category | natural key, key, surrogate key, sdtm domain, variable list, missing value, data validation rules, data governance, reviewer guide, cdisc | source presentation source |
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| 97 | Handling Missing Data in Clinical Trials: Techniques and Methods | Pennidhi Karlakunta, Naveen Kommuru |
The reliability and interpretability of results from randomized clinical trials are greatly influenced by the quality of data. Missing data in clinical trials can be a serious problem, especially in randomized trials where missing data could produce biased estimates and compromise inferences. The preferred and most satisfactory approach to address missing data is to identify ways to prevent it. However, missing data are ubiquitous as some of the trials could span over months or years. Since each trial has its own set of design and measurement characteristics, currently no universal method for handling missing data in clinical trials. This paper will present programming techniques and methods to handle missing data with a major focus on multiple imputation | missing value, imputation, multiple imputation, sample, clinical trial, proc step, treatment effect, likelihood function, proc mi, inferential statistics, adam dataset, confidence interval, maximum likelihood estimation, sensitivity analysis, proc means, cdisc | source presentation source |
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| 98 | How GSK Tech is Democratizing Data Science | Pragathi Venkata | As a part of the Digital transformation journey, one of the goals of GSK technology organization is to Democratize Data science. As it wouldn't be possible to have a data scientist in every room to enable that, we needed to scale our skillset to develop citizen data scientists across the company. Our guiding principle is to ask-analyze-act. We started by deploying a user research survey to ask about existing pain points. We analyzed the result and devised a strategy to act on. Our MVP (Minimal Viable Product) objectives were: 1. Provision of modern self-service Data science stack - an easily accessible way for requesting data science tools in a self-service fashion; 2. Inspiring code reuse and collaboration - by promoting usage of platforms such as GitHub and Guilds on workplace; 3. Training and development - leveraging platforms such as Pluralsight, Learnathons & Hackathons | data science, machine learning | source presentation source |
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| 99 | How to Build Compliance Mindset into A Team Habit | Xiaohui Wang | Building an efficiently synchronized high-performing programming team is always critically important to a programming group manager. Based on my years of experience in building and leading such teams in global pharmaceutical companies and my current role, I identified 5 critical areas a leader should pay great deal of attention and put effort into when building a high performing team: 1. Building discipline into the team to empower team to perform well at any circumstances, without cutting corners; 2. Building knowledge and skill sets in the team to empower the team with the capability to quickly comprehend tasks at hand and accomplish the tasks with high quality results and speed; 3. Building right process and standards to not only empower the team to accomplish tasks efficiently, also systematically reduce error rate. 4 | data cleaning, out= | source presentation source |
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| 100 | How to Handle the Challenges for the Content of Specific Datasets in FDA Submission for HIV Drugs | Fangfang Du | The FDA recommends that specific additional datasets should be submitted as part of the sponsor/applicant's application for drugs intended to treat human immunodeficiency virus (HIV). While the FDA provides detailed guidance and CDISC supplements this with a Therapeutic Area Data Standards User Guide for HIV (TAUG-HIV), there are various factors such as medication, indications and company standards which can impact the submission. This paper provides an overview of the challenges when creating these analysis datasets required for a recent submission of an HIV product, and the approaches utilized to handle the gaps between studies and guidance documents. An alternative approach from the FDA's guidance was developed using the TAUG-HIV to support the SNAPSHOT analysis and the recommended datasets | adam dataset, adverse events, clinical trial, cdisc | source presentation source |
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| 101 | How to reduce programming time for ADaM Creation? - Presenting STAG, a Tool Based Approach | Diganta Bose | In this Industry, fast track systems are needed to push m aximum "electronic submission" deliverables in minimum possible time. Keeping this "objective" in mind, it is always handy to have tools that c an process study level analysis results quickly and conveniently with minimal "study specific or data d riven" programming intervention. SDTM To ADaM Generator Tool, or STAG, sets the example of " tool based ADAM automation " that directly intends to contribute towards enhancing productivity of CDISC based submissions to regulatory authorities. The tool, at front end, has a User Interface that asks the user to fill out certain fields so that study level analysis requirements can be conveniently specified in a simple and succinct manner. Once the application is run, SAS Program files are generated. These SAS program files will contain codes that can be utilized to create the most appropriate and important ADaM variables based on the entries in the UI | sample, sdtm domain, adam dataset, missing value, excel, clinical trial, cdisc | source presentation source |
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| 102 | Implementation of Metadata Repository & Protocol Automation at Bayer Pharma | Girish Rajeev, Julius Kusserow |
Bayer Pharma has recently implemented a Metadata Repository (MDR) for global use across Bayer Pharma for defining, governing, managing and consuming structured medical standards. The medical standards are used for several downstream processes and the MDR is used to send machine-readable medical standards to other systems both hosted internally at Bayer or externally by vendors such as Electronic Data Capture and Clinical Data Repository. Also in parallel, Bayer developing a system form structured Protocol Authoring & Automation Tool (PAAT). The PAAT keeps track of constraints and connections between study design elements such as Objectives, Endpoints/Variables, Inclusion/Exclusion Criteria, Activities and Trial Parameters. With native integration with the MDR, Company and industry approved clinical trial standards can be incorporated early on in the study design process | data validation rules, data dictionary | source presentation source |
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| 103 | Implementing New Technical Guidance: A Sponsor Experience with the April 2018 CBER Vaccine Guidance | Liping Zhang, Sandra VanPelt Ngyuen |
For organizations with highly developed data standards, processes, and governance, it can be extremely challenging to keep up with the continuously growing and evolving requirements and guidance published by the various regulatory agencies and standards-developing organizations. Standards are designed to support compliance with some degree of flexibility, but in practice, making timely changes to standards which are actively being utilized in clinical trials can present multiple challenges which need to be assessed and addressed. This paper presents Pfizer's experience with implementation of CBER's April 2018 Guidance for Industry "Submitting Study Datasets for Vaccines to the Office of Vaccines Research and Review"1: highlighting our process along with some of the challenges and considerations and making some recommendations for organizations when incorporating newly released guidance or standards | data integration, data standard, sample, adverse events, clinical trial, concomitant medications | source presentation source |
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| 104 | Improving the Attractiveness of a CRO Career: The PHUSE CRO Summit | Christopher C. Hurley, Jeffrey M. Abolafia |
The PhUSE CRO Summit was formed to identify the most critical issues facing CROs today and to determine how CROs can collaborate with PhUSE to address these issues. Leaders from top CROs gathered in focused discussion that included elements of Design Thinking and the Theory of Constraints Logical Thinking Processes to define the goals, objectives, and necessary conditions to solve our problem. Using these methodologies, the team decided on the goal of "improving the profile and attractiveness of a CRO career by fostering a positive culture that people want to join". This poster will delineate the processes utilized by the group. We will describe the techniques used to choose our goal, our objectives, and the necessary conditions and obstacles to overcome for reaching our goal. The reader should come away with a clear understanding or our road map and project plan | pharmaceutical industry, problem solving | source poster source |
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| 105 | Incidence Rate - Incidence Proportion Prevalence Rate and Other Related Statistics | Nadiia Pukhliar | There is a tangle with incidence related statistics in clinical trial reporting. In the analysis of Adverse Events one often uses "incidence rate", but actually means "incidence proportion". Whereas incidence density rate is the same as incidence rate with only one qualification: denominator is based on subject-time at risk. The algorithm for time at risk calculation is completely different for Adverse Events analysis compared with PD or death analysis. For Adverse Events analysis, FDA uses terminology "incidence rate" alongside with "reporting rate". How to sort out which statistics name implies the correct object is an open question. The choice of inappropriate algorithm leads to undercounting number of events. Statistics for incidence can be queried for both safety and efficacy analysis. While working on TLFs for reporting, we found out that there are various approaches and different meanings for the same statistics depending on the context and analysis needs | adverse events, proc sql, create table, group by, proc step, order by, proc freq, clinical trial | source presentation source |
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| 106 | Industry Experiences Submitting Standardized Study Data to Regulatory Authorities | Janet Low, Vincent Guo |
Food and Drug Administration (FDA) and Pharmaceuticals and Medical Devices Agency (PMDA) require standardized study data for certain regulatory submissions. Industry approaches to meeting these requirements vary across companies and depend on regulatory review divisions. PHUSE's Optimizing the Use of Data Standards established Industry Experiences Submitting Standardized Study Data to Regulatory Authorities project to share industry experiences and recommend best submission strategy and planning practices. The paper starts with an overview of submission activities that outlines the scope of the submission work; discusses regulatory requirements and differences between FDA and PMDA submissions; discusses legacy data conversion; and explores the ways to incorporate and optimize the use of the Study Data Standardization Plan (SDSP) | data standard, data validation rules, data cleaning, data transformation, clinical research, clinical trial, data governance, regulatory submission, adam dataset, phase ii, cdisc | source presentation source |
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| 107 | Integration of Biomarkers Data in Clinical Trials | Prakash Dev | Center for Drug Evaluation and Research (CDER) has shown increased involvement in the biomarker development in recent years resulting in the establishment of CDER's biomarker qualification program. This program provides an evidentiary framework to support qualification of biomarkers as a surrogate endpoint for an established context of use without requiring re-qualification of the biomarker. This action has resulted in interest from various biotechnological and pharmaceutical companies in biomarker development. CDER has expressed its goal of using biomarkers to add supporting information to clinical trials outcomes. It will provide a framework for the review of biomarkers for use in regulatory decision-making. As attrition in the drug development has become a frequent occurrence, so have the use of biomarkers as surrogate endpoints in clinical research and target identification | clinical trial, clinical research, clinical endpoint, surrogate endpoint, sample, data science | source presentation source |
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| 108 | Interactive Adverse Event Data Visualization Using R Shiny | Aditya Rachoori, Mukul K Mittal |
AE data is critical data in drug discovery process, monitored for the patient safety and to take critical decisions regarding continuation of subjects in clinical trial. Reviewers look for parameter such as treatment emergent, seriousness, relatedness and toxicity grade. The traditional AE reports consist of basic summary tables and listings. It is difficult to detect AE patterns in a table presentation. Interactive visual analytics present a useful alternative to static outputs for exploring safety data and present a great opportunity to enhance evaluation of drug safety. In this paper we would use R -Shiny application to create interactive dashboards based on different parameters, which would help the statisticians to have comprehensive view of the Adverse events as well as they can a lso look at more granular level. Overall R -Shiny app would provide the detailed AE information at patient level/study level as interactive graphs and tables | adverse events, trial elements, system organ class, heatmap, ctcae grade | source | 9 |
| 109 | Interactive Real-world Data Analysis Using Machine Learning Algorithms | James J. Kim | The MDV (Medical Data Vision) database is based on health claims, administrative and DPC (Diagnosis Procedure Combination) data from over 300 Japanese hospitals which is used to investigate and research the Japanese market including pharmacovigilance and epidemiology studies. As of January 2020, it contains approximately 30 million patient records, 1.7 billion disease records and 9.2 billion activity (treatment, intervention, etc.) records. In RWD analysis, common work is to clarify actual usage of the target drug. For example, we like to know demographics (i.e. age, weight and etc.), frequently used dosage, common tests for diagnosing diseases or concomitant medications. It would be ideal to generate descriptive analysis results identifying the primary factors, yet it is challenging to find common patterns in complex conditions involving multiple diseases, medications and tests | proc hpsplit, sensitivity analysis, type i error, feature importance, roc curve, sample | source poster source |
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| 110 | Is Artificial Intelligence our destiny? Data-driven Decision-making in a Dynamic Landscape using SAS Viya | Aman Bahl, Hrideep Antony |
Health is not something that happens a couple of times a year when you visit doctors. Health is something that happens every day. It is happening right now. It is cumulative of choices, lifestyles. Artificial Intelligence (AI) is making its way into the realm of clinical trials. While a lot of discussions seems to be centered on clinical trial recruitment and using AI to mine electronic medical records (EHRs), but it seems to only scratch the surface. Experts predict monitoring drug adherence, pre-emptive risk monitoring, decision-making, diagnostics, and process optimization are some of the other areas where the technology is expected to make an impact in future. In this paper, we will explore the benefits of SAS Viya from a machine learning and AI perspective. We will briefly cover various tasks in the analytics life cycle and show how to create a decision tree model as an example using SAS Viya Studio | sas viya, machine learning, proc hpsplit, artificial intelligence, proc hpforest, gradient boosting | source presentation source |
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| 111 | Is R language reliable and efficient tool for programming SAS datasets or just art for art's sake? | Piotr Podlewski | R is the open source software environment and language used for data analysis and statistical computing. A great deal has already been said and written about R's wide variety of graphical and statistical techniques. Even though R as programming language is constantly growing in popularity in pharmaceutical industry, it is still qui te unpopular to use R in preliminary stages of researches like importing data from different sources, tidying it, calculating new variables in datasets and making other amendments available in SAS data steps. This paper explores the ways R can come in useful as a tool for programming datasets and compares both tools in terms of performance and ease of use. It will also focus on reliability of packages (from CRAN repository, Bioconductor and other sources) that one can use when creating and modifying datasets in R | proc sql, proc step, clinical trial, pharmaceutical industry, data step, dataset, clinical research, left join, data science, data cleaning, proc sort | source presentation source |
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| 112 | Lab Master List File Makes Easy | Clio Wu | Clinical laboratory data plays a significant role in the clinical trials, the primary objectives for collecting lab data are to evaluate the safety of the patients in the trials and to demonstrate the efficacy of the investigational medications. As Lab data come from central and local laboratories with various format of units and lab parameters, converting lab data into a submission-ready SDTM LB dataset has always been a challenge for sponsors and their CRO partners. This paper details a standard lab master list file and associated programming process to make the lab converting process easy, the project was initiated in year 2012 to resolve lab units and lab parameters related challenges where data collected from various lab vendors across multiple sponsors and TAs. This file is a living document to collect and process new identified lab units and new lab tests terms overtime to this day | laboratory data, raw data, cdisc controlled terminology, clinical trial, ctcae grade, mock shell, data science, cdisc | source presentation source |
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| 113 | Let Us Trace Back to the Beginning from the End | Madhusudhan Reddy Papasani, Swathi Kotla |
Integrity of regulatory submissions relies on traceability, allowing regulators to check source data and understand the processes involved in the transformation of data, to the analysis results. CDISC standards, along with the Define-XML, are interconnected and designed to provide relationships between analysis results, analysis data and SDTM datasets. In this paper, a discussion of data standards and traceability is reviewed using an analysis table from an oncology Overall Survival (OS) analysis to explore traceability from the analysis results back to the case report form (CRF) collected data. As part of this process, interrelationships between analysis results (AR) to analysis results metadata (ARM), ARM to ADaM datasets, ADaM datasets to SDTM datasets and finally SDTM datasets to the CRF will be presented, traced and highlighted here in the same way a regulatory reviewer would trace results to the source data in a submission review | adam dataset, sdtm domain, overall survival, log-rank test, rank test, temporary dataset, annotated crf, data standard, clinical trial, regulatory submission, raw data, data science, randomization, cdisc | source presentation source |
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| 114 | Let your SDTM or ADaM SAS code update itself whenever the Specs change | Joseph W. Hinson | Statistical programmers typically use SDTM and ADaM specifications laid out in an MS EXCEL workbook as a guide for developing SDTM or ADaM dataset SAS programs. But this "Specs" document usually undergoes several changes throughout the study lifecycle. Often the changes are made by the lead programmer without notifying production and validation programmers. The paper uses PROC SQL and the PE domain as examples for updating the variable attributes LABEL, TYPE, and FORMAT whenever the dataset creation program is run. This is made possible by embedding macro calls in the PROC SQL code. The paper describes how the macro calls use DOSUBL and %SYSFUNC to perform virtual "typing" of code keywords in the PROC SQL code. Such a dynamic code update technique can make SDTM and ADaM programs instantly updated whenever run, thereby reducing coding time as well as preventing mismatches between dataset variable attributes and their specifications |
proc sql, proc step, macro call, macro variable, excel, input statement, create table, macro function, cdisc | source presentation source |
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| 115 | Leveraging Metadata for SDTM QC Automation | Sunil Kumar Gupta | Seasonal SAS programmers know how to leverage SAS for automation by taking advantage of metadata and SAS macro programming. Accessing metadata of datasets and SAS programs to automate and standardize processing of multiple files can be achieved without manual efforts. Combined with SAS macro programming, automation al lows for more robust and advanced programming techniques to handle messy data as well as unexpected changes in data input. In a dynamic environment, this helps to assure confidence in processing large amount of data to convert into meaningful information and action items | proc step, proc compare, proc sql, macro definition, cdisc | source presentation source |
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| 116 | Leveraging Python to Generate Define-XML v2.0 | Kevin Miller, Larry Sleeper |
In our current SAS-dominated world of statistical programming of datasets and define.xml files, Gilead has sought to implement emerging Python technology as an efficient alternative. Specifically, we have leveraged an existing standard metadata template commonly used in industry today for CDISC validation as the starting point for our new Python utility. We have also built in quality-by-design functionality (i.e. upfront quality control checks) so that the user can even more efficiently generate a define.xml v2.0 compliant file for regulatory submissions, allowing for inclusion of analysis results metadata (ARM), automated CRF page numbering, and other benefits of automation. This paper will discuss the various Python modules used and provide the general process flow for use of this Python utility for future submissions | define-xml, excel, data cleaning, cdisc | source | 5 |
| 117 | Life Science's Process and Artifact Generation Automation | Lucky Singh | A metadata-driven platform is paving the way for end-to-end automation of the Clinical Drug Development Process. Leveraging Artificial Intelligence (AI) and Machine Learning (ML) concepts with a focus on standards and metadata management, this platform aims to enhance the curation of metadata and streamline the delivery of artifacts ranging from protocols through Clinical Study Reports (CSRs). The primary objective of this platform is to have Subject Matter Experts focus on curation of metadata needed to guide the automated production of artifacts, that are generated repetitively as part of Clinical Study Lifecycle. This focus consequently enhances the value of the metadata and related transformations making it possible to extend the capabilities of the platform to cover additional artifacts, processes, and use cases | metadata management, clinical trial | source | 2 |
| 118 | LOCF Programming with 6-step Index Mapping | Homer Wang | Last Observation Carry Forward (LOCF) is a common method in efficacy analysis. It is error-prone and time consuming due to different methods and data situations. The 6- step index mapping method is introduced in this article which includes: 1. Create LOCF dataset with qualified LOCF feeding records; 2. Define desired time points; 3. Identify the missing time points by index; 4. Map in the missing time points by index; 5. Define the created LOCF data structure; 6. Define the keeping/dropping variables. This method has been implemented in multiple studies and proved to be accurate and time saving | data step, last observation carried forward | source presentation source |
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| 119 | Machine Learning for CDISC Specification Recommendations | S. Preston Burns | The creation of SDTM and ADaM variable specifications is a painful requirement for enjoying the benefits of CDISC standardization. Demanding large amounts of both statistician and programmer time, it is an expensive process that grows more costly as the quantity of required metadata increases. The design phase of the dataset specification process, in particular, appears ripe for automation. Previously, Rho began work to leverage historical specification data to generate recommendations for programmers entering ADaM specifications for new studies. Here we build on the text mining methods introduced in the previous work by applying creative data preparation, feature development, and machine learning models to the predictive task. These developments allow us to incorporate more inputs, and the interactions between them, in the recommendation of variable specifications | machine learning, proc hpforest, champion model, web service, logistic regression, excel, logistic distribution, adverse events, cdisc | source presentation source |
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| 120 | Machine Learning-AI is the Future - What Now? | Priyanka Adusumilli, Priyank Patel |
The pharmaceutical industry is investing vast amount of money in R&D to bring ML-AI driven solutions to enhance overall End-to-End Clinical Trials experience. The ML -AI based driven solutions will change the approach to the current new drug-biologics application submissions to the regulatory authorities however they are still costly and will still take some time to become industry standard. What's the plan in the interim? Within clinical data operations, the industry invests heavily on the global data collection standards to enhance the data collection experience, bringing pseudo -automation into the current process. This helps foster consistency within the data collection process, expedite CDISC SDTM compliant package creation and ease the analysis component down-stream. There are tools available that highlight pseudo-automation with expediated quality outcomes which have already been proven effective | machine learning, clinical trial, clinical data, clinical research, raw data, cdisc | source poster source |
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| 121 | MACRO TRIO IN SAS - DATASET COMPARISON AND REPORTING | Nagaraju Mancha | SAS is a great tool having macros to automate the repeated tasks. Programmers can develop macros aiding in completing the regular tasks within a short span of time with good quality. Programmers especially validation teams have to validate datasets by comparing validation datasets with production datasets to identify the issues or updates in production datasets. Comparing individual datasets might be easy but if there is a need to compare all datasets at once, definitely it would be a challenge. To overcome this challenge, we can take advantage of macros and automation. This paper illustrates a group of macros which will helps programmers to compare all the datasets of libraries at once and create the report which contains mismatches or updates of datasets w ith clear explanations. The output report of this program will have variable, structural, label and data differences thereby we can improve quality of data and tracing the updates | proc compare, excel, ods excel, proc step, error message, record count, data cleaning, sas log | source presentation source |
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| 122 | Making the Invisible, Visible: People, Ideas and Conferences | Gayathri Kolandaivelu | Only those who can see the invisible can do the impossible. Novel innovation in the healthcare industry is a combination of people and ideas. Conferences and many professional societies play a vital role in making these connections. People from different stages of career come together for a common purpose or theme, share their ideas, a process that they are developing, and procedures that have yielded positive outcomes. As an entity in the skilled community, networking and connections are vital to enhancing both our professional and personal development dramatically. The need to be connected is essential in the information age to foster the visionaries who can envision innovation, that helps the entire health care community and, ultimately, the patients. Analytics generated over a few years through PHUSE membership across industries will be shared | pharmaceutical industry, clinical trial | source poster source |
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| 123 | Managing Data Flow for Wearables, Apps and Sensors | Mayur Tripathi, Prashanth Jayadevamurthy |
clinical research, clinical trial | source presentation source |
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| 124 | Managing Simultaneous Drug Submissions with Breakthrough Designations - Lessons Learned from a Programming Perspective | LiFeng Good | In March 2018, the Ministry of Labor Health and Welfare (MHLW) in Japan granted SAKIGAKE designation to Tafamidis for treatment of patients with transthyretin cardiomyopathy, a rare, fatal, and underdiagnosed condition associated with progressive heart failure. Two months later, in May 2018, Pfizer received breakthrough designation (BTD) from the Food and Drug Administration (FDA) for Tafamidis for this indication. Simultaneous submission to multiple agencies is challenging to begin with and requires upfront planning, harmonization, interaction and negotiation with internal/external customers and careful resource planning | clinical trial, data standard, adam dataset, cdisc | source presentation source |
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| 125 | Metadata-based Auto-programming Process - Part 3: Virtual AI Assistant to Automate - TLG Generation | Harry Chen, Shobha Rani |
Traditionally the programming process for statistical report generation relies heavily on manual work as we need to read and understand human language specification documents manually. Adopting Machine Learning and leveraging the power of NLP, we developed virtual assistant system to optimize report generation process with limited human intervention. We build this by enhancing RASA open source machine learning framework with Named Entity Recognition neur al models. The virtual assistant can analyze the TLG specification as user request and tackles these tasks with natural language understanding/dialogue management leading to identification of contextual named entities which can then feed the predictive mod els and decide the next best action for the right report generation. To train the ML model, we scanned trial folders from 200+ compounds and collected 150K report titles, 2M macro call to learn the links between human language specification and the final programming codes | machine learning, training set, macro call, next best action, probability functions | source presentation source |
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| 126 | Migrating to LSAF - The Danone Nutricia Research Journey | Jean-Marc Ferran, Paul Vervuren |
clinical data, clinical research, clinical trial, data migration, life sciences | source presentation source |
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| 127 | Mind the Gap: Raw Data Model | Julie Smiley | Increasingly more organizations are implementing metadata management solutions as part of their overall clinical data standards and information governance strategy. The objective is to manage standards, including mappings and transformations across the clinical data lifecycle to drive efficiencies and automation in study setup and programming. However, one of the major barriers in achieving this goal is the gap between data collection and SDTM. While CDASH is the standard for data collection, it only addresses the needs for defining the data collection forms, but there is no standard for raw data output from source systems. In this session, we will discuss this gap, the impact it has on the overall data flow, and strategies for mitigation | clinical data, raw data, clinical programming, data standard, sdtm programming, excel, cdisc | source presentation source |
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| 128 | Mindset for Effective Collaboration and Success in a Multinational Remote Team | Alona Bulychova | The modern world is full of opportunities. Globalization and digitization make possible to work together with completely diff erent people from different cultures, separated by great distances and time zones . But every human is a person with their own value s, vision, level of involvement, function, experience, habits, goals and desires. Therefore, when these people become a Team, a real mission for the Team Leader and Manager arises: how to create an effective collaboration between every team member, how to turn so many different factors from chaos into an opportunity for development, growth and self - motivation, and besides how to achieve the Success. The key ideas which will be presented here include everyday challenges and good practices, personal developm ent strategies, key principles of thinking and communication, and the real power of having a successful mindset in any team | source presentation source |
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| 129 | Natural Language Processing for Enhanced Treatment Decision | Phani Ponnapalli, Subrahmanyam Rayaprolu |
Data Driven Science is opening new opportunities for assimilation and evaluation of large amounts of complex health- care data. Artificial intelligence (AI) based technologies such as Natural Language Processing (NLP) provide an opportunity for Life Science firms to extract the key facts from structured and unstructured data and transforming real world data into actionable intelligence for decision making. This paper introduces diverse data types for clinical decision support (eg, Genomics, Electronic Health Record, Physician notes, Lab data, and Clinical trial data), concepts of AI/Machine Learning and provides deep insight on Natural Language processing algorithms. Further, this paper explores Data-Driven architecture to integrate diverse Clinical data using Hadoop framework | clinical trial, clinical data, machine learning, clinical database | source presentation source |
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| 130 | No more merging! (property graph database demystified) | Alexey Kuznetsov | How many times have you heard that graph database is an excellent tool, but you could not imagine how these "bubbles" could help to make sense of your data? In this paper I will start with demonstrating "traversal" of clinical trial data using a small table-based self-written data visualization tool with a graph data model in the backend. A use-case is presented to demonstrate the advantages of using graph database for performing searches, looking at data from various perspectives as well as storing and retrieving different data sources and types in one place. The demo will be followed by describing basic principles of graph database and Cypher. Technologies used: Neo4j (graph database), Cypher(graph query language), Python (+visualization package: plotly dash) | adverse events, concomitant medications, clinical trial, proc sql | source presentation source |
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| 131 | Oncology Data Through Customized Graphics | Hima Bhatis | Poster | source | 1 | |
| 132 | Open Source Technologies in Clinical Research | Bruce Wienckowski, Michael S Rimler |
Though prominent in many other industries, open source technology is just now starting to have an emerging presence in pharmaceutical drug development. These technologies offer an enormous amount of potential toward task automation capabilities, cutting edge machine learning and statistics, and advanced dynamic data visualization platforms. However, given the highly regulated nature of the industry, this potential comes with a burden of responsibility to ensure that results are validated and reproducible. Open Source Technologies in Clinical Research (OSTCR) is a new project under the PHUSE working group Emerging Trends and Technologies. Our goal is to broaden the acceptance and general level of comfort with these technologies in the industry to assist in increasing their level of adoption. Our approach is to demonstrate value through use cases, while providing implementation and validation guidance along with best practices | data cleaning, data science, clinical research, quality assurance | source presentation source |
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| 133 | Opening Doors for Automation with Python and REST: A SharePoint Example | Michael Stackhouse | A large part of increasing the efficiency of a process is finding new ways to automate. If manual components of a process can be replaced with automation, those components can then move to the background where they can be validated and trusted to be reliable. Large swaths of data sit within company intranets or other services that may seem 'disconnected' - but they may be more connected than one might think. This paper will explore extending automation capabilities with Python and REST APIs, using SharePoint as a primary example. Some topics covered will include using Python to make web requests, the basics of authentication, and how to interact with REST APIs. The paper will demonstrate how data repositories that may seem disconnected can be integrated into automated processes, opening doors for new data pipelines and data sources to pave the way for process improvements, efficiency increases, and better information | rest api, clinical trial | source presentation source |
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| 134 | Overcoming Hurdles in Clinical Programming Project Management Using Microsoft Teams | Miten Tiwari | Clinical trials are becoming increasingly complex. Clinical programming project management requires planning, initiation, execution, and closure by working with multiple cross-functional teams under strict timelines. Collaborating across diverse systems can lead to delays and decreased productivity. Monitoring project status across different resourcing models makes workflow overview challenging. Gleaning essential information from communications (emails, chats, meeting minutes, etc.) takes a long time. High volume and diversity of trial documents contribute to inefficiencies. Working documents requiring to be updated and shared by multiple users in real time can be laborious. Evolving trial complexity and increasing cross-functional team communication has created a need for a project work-space where projects can be boot-strapped to address scattered information | clinical programming, clinical trial, excel | source presentation source |
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| 135 | Patient Privacy - The Topmost Priority! | Shiva Gogu | pii, clinical trial | source presentation source |
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| 136 | Penalized Likelihood Logistic Regression for Sparse Data Using Data Priors | Martin Dörr | Penalization of the likelihood is a probate means to stabilize the estimate when fitting logistical regression models with sparse data. The default Firth method, available in PROC LOGISTIC, shows good results in bias and MSE properties for the parameters. However, if one is interested in several marginalized endpoints, a switch to PROC NLMIXED is necessary. Here the Firth method cannot be implemented. A suitable alternative are logF(1,1) data priors. This presentation will introduce a logistic regression on sparse data with supporting data priors which demonstrate the custom PROC NLMIXED code for modeling |
proc step, likelihood function, logistic distribution, logistic regression, proc logistic, regression analysis, proc calis | source presentation source |
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| 137 | Population PK Dataset: Basic Concepts and Challenges from the Perspective of a SAS Programmer | Namrata Pokhrel | Population Pharmacokinetic (Pop PK) Data analysis is a recent requirement in drug submission to the regulatory board . The purpose of Pop PK data set is to support PK modeling and simulation by creating a regulatory compliant NONMEM data set. In this paper, I will be presenting the basic concepts of Pop PK data set and challenges to create a robust Pop PK data set from SAS programming perspective | key, sample, raw data, data cleaning, imputation, clinical trial, clinical research, population pk, cpu time, concomitant medications, data science | source presentation source |
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| 138 | Poster - PP23 | Mike Tan, Richard Addy, Deidre Kreifels |
The annotated Case Report Form (aCRF.pdf) is a crucial component in a data submission package. The variable and value annotations on the aCRF are linked to PDF page references in the define.xml, allowing reviewers to quickly associate submission data with the collection forms. For that to work, aCRF annotations and define.xml origins must be consistent. Aligning CRF page references in the define.xml with the aCRF annotation, and keeping them aligned, is an arduous task if handled manually. In this poster, we will share a programmatic approach that extracts CRF annotations (including pdf page numbers) into a SAS dataset. Previous literatures suggested exporting CRF annotations in FDF format. However, we found that exporting as XFDF format is more user-friendly because of its XML-like nature. We will share a CRF annotation schema that allows for efficient extraction by SAS programs | dataset, crf, cdisc | source poster source |
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| 139 | Programmatic Generation of Health Canada's Comparative Bioavailability Information Appendix B | Michael S Rimler | dataset, data cleaning, proc contents, proc step | source | 15 | |
| 140 | Programmer to SME - Career Game Changer | Divya Cetlur, Ephicacy Lifescience |
Imagine an intersection in a Venn diagram, where one circle represents "Programming" and the other "Management" - the intersection is what we would call "Subject Matter Expert (SME)". Subject matter experts are individuals, who are competent to perform the work hands-on as well as manage the work - gauging the complexity and time required, thus enabling effective study management. SMEs shoulder major risk in successful delivery and hence, have become popular touch points for the leadership or project managers in the recent times. Their work revolves around 3 major productivity targets: a) complexity of the task, b) resource/task allocation to individuals in the study team, c) quality and timeliness of the tasks/report objects delivered. This paper will discuss few career-game changing scenarios and pointers to evolve from a programmer to an effective SME in today's programming world | mock shell, clinical trial | source presentation source |
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| 141 | Project Programmer @GSK...the Journey Continues! | Eileen Ching, Raghu Kumbharathi |
The role of the Clinical Programmer continues to evolve and adapt due to increased pressure from regulatory demands; novel technology and growing complexities resulting in the need for stronger technical; operational and strategic leaders who can influence at the project level. The journey to implement and embed the PP role within our industry is in its next phase of execution in our organization. Clinical Programming at GSK has laid out a broad vision for PPs to be leaders of Biostatistics operational excellence; driving strategic programming execution throughout the lifecycle of an asset. In this paper, we present some of the tangible aspects of the journey thus far; these include the elevation of the role expectations; strengthening the training curriculum; developing a communication strategy that embeds our role; creating a fit- for-purpose resourcing tool; and finally establishing a formal stakeholder management framework to enhance our engagement with stakeholders | clinical programming, clinical research, excel | source presentation source |
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| 142 | Quarterly Safety Reporting: Timely Analysis of Clinical Safety Data Using Qlik Sense Software | Yvonne Denkins | FDA guidance strongly recommends a process for reviewing, evaluating, and managing accumulating safety data within the clinical data base at appropriate intervals (21 CFR 312.32(1)(i))1, 2. To meet client requests for review of safety data per agency guidelines, Clinical Scientists (CS) at IQVIA Biotech created and developed a robust process for quarterly safety reporting. Clinical Science and other team members review study data quarterly using a validated Qlik Sense software platform. The resulting Quarterly Safety Report (QSR) is prepared as a stand-alone document showing case report form safety data using tables and graphics. Any anomalies, trends, and safety signals found are reported to the client and if needed, regulatory agencies. The IQVIA Biotech Qlik Sense software platform is a visual tool that incorporates data from multiple sources into a user-friendly, interactive, dashboard environment | clinical data, data review, vital signs, adverse events, clinical trial | source presentation source |
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| 143 | R & Python for Clinical Workflows | Philip Bowsher | The statistical programming space is evolving. Many universities and programs are educating students about Clinical Data Science. Moreover, the field is popular among recent graduates. R, Python, SQL and Javascript are important programming languages within the Clinical Data Science toolbox. For years, people have wondered which language will take the top seat but It is clear now that a new topic has taken focus… Interoperability and the capability of these different languages to work together harmoniously. If you review any data science job application, you will likely see R and Python listed together among the technologies to know. The information below will focus on the interoperability of the top 2 programming languages in the data science space, R and Python, and how they can be combined to achieve great results for your clinical workflow | data science, deep learning, statistical programmer, machine learning, clinical research, clinical programming | source | 13 |
| 144 | R for Deep Learning with Tensorflow with Applications in Cancer Immunotherapy | Philip Bowsher | You can access the examples below live with this link: ● https://rstudio.cloud/project/1011147 Examples are in the immunotherapy folder . The project above is based on Will Landau's interactive workshop on drake. It has most of the R packages (Tensorflow and Keras) we need for the immunotherapy analysis below in this paper. If you like, you can go to the drake workshop link below: https://rstudio.cloud/project/627076 Then you can add the immunotherapy examples by downloading a zip folder from here and then adding uploading it to the drake session above: https://github.com/philbowsher/phuse-ML12-R-Tensorflow-Immunotherapy Interest in deep learning is increasing in the drug development space. Many clinical statistical programmers use R and or SAS. When Tensorflow (an open source library for numerical computation created by Google) was originally released, the bindings however were in python | deep learning, statistical programmer, machine learning, neural network | source | 14 |
| 145 | R Package Oriented Software Development Life Cycle in Regulated Clinical Trial Environments | Rinki Jajoo, Yalin Zhu |
data cleaning, clinical trial, statistical programmer, independent qc, r package | source presentation source |
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| 146 | R Shiny Applications Within Ophthalmology | Julia Dedic | With the increasing need for faster and more efficient exploratory analysis of clinical trial data, R Shiny provides a framework that does just that. The time consuming task of re-running code to further investigate and report on deliverables is replaced with this interactive web application. This not only makes workflow faster, but it also provides a convenient feature; the ease of access and usage of data and results through a web URL. Through the creation of packages, the R Shiny applications can be streamlined to fit the outputs required for different therapeutic areas. In this paper, the work presented will cover a package created for Ophthalmology. This package will contain reproducible code containing modules to create tables, listings, and graphs for analyzing Ophthalmology studies in a dynamic setting. Within these modules, dynamic filtering options will allow the user to explore the data with specifications predefined | standard deviation, dataset, quality control, spaghetti plot, adam dataset | source presentation source |
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| 147 | R syntax for SAS programmers | Max Cherny | Making the transition from SAS programming to R programming can be quite challenging, especially for SAS programmers who have never coded in an object- oriented language. However, learning to code in R does not have to be difficult for those already fluent in SAS. Both languages provide similar functionality, with the main difference being the syntax required to accomplish the same objective. This paper provides a side-by-side comparison of R and SAS syntax, using examples of a clinical programmer's typical SAS code adjacent to the corresponding R syntax. These easy and reusable examples can function either as stand-alone programs or as starting points for further acquisition of R fluency for the SAS programmer | character variable, numeric variable, sas programmer, sas syntax, proc step, proc transpose, sas programming, data science | source presentation source |
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| 148 | Real-time Oncology Review - Applicant Experience | Annie Varghese | The Food and Drug Administration (FDA) Oncology Center of Excellence Real-Time Oncology Review (RTOR) pilot program aims to explore a more efficient review process to ensure that safe and effective treatments are available to patients as early as possible, while maintaining and improving review quality and balancing the review team's workload through data and analysis standardization, and early iterative engagement with the applicant. At the time of top-line results of a pivotal trial, if certain eligibility criteria are met, an applicant can apply for the RTOR pilot by notifying the appropriate division Regulatory Project Manager. Janssen applied for this pilot program for 2 supplemental filings in 2019. This paper covers our experience participating in this program. We also cover the recommended timeframe for FDA interactions and provision of submission components and specific examples of how such deliverables were completed for the representative submissions | adam dataset, clinical endpoint, cdisc | source presentation source |
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| 149 | Recommendations for GDPR Compliancy by the PHUSE Data Transparency Working Group GDPR Project | Arlene Coleman, Shannon Labout |
The PhUSE Data Transparency Working Group GDPR Project team is writing a white paper with recommendations for applying GDPR rules to data clinical research studies. This paper has been prepared by the GDPR Project sub- team leads with the intent to list and discuss the research process and recommendations that will be summarized in the PhUSE GDPR White Paper | clinical research, privacy | source poster source |
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| 150 | Regulatory Report Automation with R | Shimeng Huang, Suwen Li |
csr, adam dataset | source presentation source |
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| 151 | Regulatory Scrutiny on Data Integrity: Are You Safe? | Chris Decker, John Horton |
Over the past decade we have helped dozens of life sciences clients identify, design, and implement their statistical computing environment (SCE) leveraging the best of breed technologies and optimizing their processes. During the past few years we have noticed a higher level of scrutiny by regulatory authorities around the clinical data lifecycle relative to an SCE. Our clients have been given formal warnings around topics such as unauthorized access, lack of traceability, and overall data integrity. These issues and how the regulatory authorities choose to enforce them could cause significant issues for pharmaceutical companies and delays in submission costing millions of dollars | data integrity, 21 cfr part 11, life sciences, audit trail, clinical data, data cleaning | source presentation source |
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| 152 | Reproducible Open-source Environments for Advance Analytics | Sean Lopp | The pharmaceutical industry is increasingly using open source tools like R and Python for all phases of drug devel- opment including early stage research and late stage clinical trials. This adoption of open source tools leads to a pressing concern and need for reproducible analysis. Unlike legacy tools, reproducibility in the open source ecosys- tem is more complex. Analysis and platforms must account for many mixed components: operating systems, lan- guages, packages, source code, and data products such as reports and web applications. This paper presents strategies organizations can adopt to build reproducible, open source analysis along with tools necessary to imple- ment these strategies | data science, pharmaceutical industry | source presentation source |
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| 153 | Rerouting Your Risk-based Monitoring Strategy: Overcoming Implementation Roadblocks with New Technologies and Approaches | Jonathan Andrus | data review, clinical research, data governance, data cleaning, raw data | source presentation source |
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| 154 | Sample Size Re-estimation in Action: Design Consideration, Charter Development and Implementation | Adam Hamm | Poster | conditional power, sample size, interim analysis, sample, sample size re-estimation | source | 1 |
| 155 | Smart, Digital Analysis & Reporting (A&R) Environments: They Are Upon Us | Christopher J. Colangelo | data science, likelihood function | source presentation source |
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| 156 | SmartLog - A UNIX based Utility | Satish Honrao | Clinical trial sponsors are seeking regulatory approvals from authorities like USFDA and EMEA. In order to accomplish this, Sponsors must submit detailed clinical study reports (CSRs) and individual participant data. Drug companies would like to do this with minimum possible time without compromising the quality. For CSR submissions, we have to submit the SAS codes, logs and listings of the generated clinical tables, listings and graphs, to the regulatory authorities. This paper will explore automated approach to submit SAS codes and collating all the issues found in the log | csr, sas base | source presentation source |
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| 157 | SPA - A catalyst for clinical protocol development | Suhas Giriraj, Ephicacy Lifescience |
In the clinical domain, Clinical Development Plan (CDP) provides an overview of the drug development strategy for all indications and describes the plan for the clinical studies to be carried out. A tool christened Structured Protocol Authoring (SPA) is a cloud-based SaaS application for collaboration space, allowing better and easier input from stakeholders aiding in developing a robust protocol. This tool is used to capture protocols in machine-readable form, saving time by promoting reuse of protocol elements from standards or past studies, configuring CDP structure and document templates, thus improve quality and preventing amendments through comparison against benchmarks and competitor studies; being in sync with medical writing with familiar Microsoft Office Online and visualizations in powerful layouts views(Gantt charts or tables). SPA's audit trail feature captures every change made in structured study design and CDPs | clinical research, clinical trial protocol, clinical trial, audit trail | source presentation source |
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| 158 | Spotfire Patient Profile Graphs: An Abridged Effective Approach Using Scripting Languages | Anish Kuriachen | The patient profile plots play a significant role in the monitoring and reporting of subject safety information from the onset of a clinical trial. Traditional patient profile plots are created based on safety data such as lab results, vital signs, drug exposure, etc. The classic patient profile graphs are usually plotted on the same x-axis using the duration of the subject in the study as the x-axis reference range. This approach occasionally leads to illegible graphics output, especially if the study has several time points or visits that collect safety information. In addition to this challenge, the traditional patient profile requires the corresponding listing outputs to interpret the subject safety profile, since graphics by itself may not express the whole story about the subject. This paper will present the use of TIBCO Spotfire to build the best patient profile with examples | spaghetti plot, clinical trial | source presentation source |
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| 159 | Standardization = Automation: A Journey from Dataset Specification to SAS Code | Anastasiia Khmelnytska | The beauty of standards is in the fact that well-defined structure screams automation. Creating datasets according to CDISC guidelines has never been easier with the macro that transforms your dataset specification into SAS code. It will make you forget about manually defining variables' attributes and parameters once and for all. The step by step guide will also cover the mappings of standard variables and parameters defined in the specification. In this paper we will discuss several approaches to creation of dataset specifications that can be used effectively for automatic code generation. Whether you are creating SDTM datasets from raw data, or mapping SDTM data to ADaM standards, with this advanced technique, you will be able to create different datasets with a single macro call - all you have to do is to choose your parameters wisely | macro variable, data step, proc step, excel, proc contents, proc format, proc import, controlled terminology, cdisc | source presentation source |
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| 160 | Standards Compliance - An Automated Way to Measure the Interoperability of Clinical Studies | Rammprasad Ganapathy | clinical trial, data standard, data validation rules, rest api, clinical data | source presentation source |
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| 161 | Statistical Analysis in an Oncology Study for PCWG3 Patients | Tony Tian | Poster: The treatments for prostate cancer becomes complex since it is with progression of the natural history of the disease and response to treatment. The initial diagnosis of prostate cancer either localized or metastatic disease can progress to metastatic hormone sensitive prostate cancer (mHSPC) or castration-resistant prostate cancer (CRPC) and ultimately leading to metastatic CRPC. This paper briefly describes the Oncology statistics analysis in clinical trial studies at Pfizer to prostate cancer based on Prostate Cancer Working Group (PCWG) criteria, which can be considered as valuable approaches for related studies to meet the analysis need in clinical trial studies | clinical trial, data science | source | 1 |
| 162 | Statistical Programming Team Contributions for RWE Regulatory Submissions | Lin Gao, Norihiko Oharu |
regulatory submission, clinical programming | source poster source |
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| 163 | SUPPQUAL Datasets: Good Bad and Ugly | Sergiy Sirichenko, Blue Bell |
SUPPQUAL datasets were designed to represent non-standard variables in SDTM tabulation data. There are many recent discussions about whether the SDTM Model should allow the addition of non-standard variables directly into General Observation Class domains instead of using SUPPQUAL datasets? However; there is still a lack of implementation metrics across the industry to understand actual utilization of SUPPQUAL datasets. In this presentation we will summarize metrics from many studies and different sponsors to produce an overall picture of utilization of SUPPQUAL datasets by the industry. We will analyze commonly used SUPPQUAL variables for being potentially promoted to standard SDTM variables. Also, we will provide and discuss examples of correct and incorrect utilization of SUPPQUAL datasets in submission data to understand if the industry is ready to switch from SUPPQUAL datasets to non-standard variables? | trial elements, adverse events, regulatory submission, data cleaning, cdisc | source presentation source |
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| 164 | Tables, Figures & Listings Metadata and Traceability | Judith Goud, Stephen Castellano |
Consistent representations of the collected data are important for internal data review as well as regulatory submissions. Many companies have developed standard analysis Tables, Listings and Figures, and PhUSE also provides shells that can be used to represent the data. Generation of standard TFLs usually involves pre-defined programs or macro calls. Two common approaches to for the generation of Listings and Tables are: 1. Content driven utilizing PROC REPORT or Tabulate and providing extra layout information 2. Layout and variable content is used to fill an output in a cell by cell fashion With organizations using a metadata repository to manage their standards, this MDR can also be used to manage the content required to automate the generation of TFLs, providing full data traceability |
statistical programmer, data lineage, independent qc, proc sql | source presentation source |
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| 165 | The Elusive Goal of Automation in SDTM Mapping: A CRO perspective | Sridhar Vijendra, Ephicacy Lifescience |
Much has been proposed and experimented with automating SDTM mapping to convert raw data (legacy/CDASH format) to CDISC SDTM standards with no or minimal human intervention. This is no easy task given the complexity and variety of studies, constantly evolving standards, and the struggle to keep up with demanding timelines. Stakes are higher in a CRO (Contract Research Organization) environment where additional variables sneak in to make SDTM automation a near impossible feat. One of the ways to automate SDTM mapping is to standardize CRF pages using a metadata repository (MDR) that allows usage of standardized mapping programs. The other option is to use machine learning and natural language processing, but this is yet to see effective implementation in practice. In this presentation, we look at SDTM mapping needs for our client to find an option that minimizes human intervention to speed up turnaround with no compromise on quality | sdtm programming, raw data, machine learning, cdisc | source presentation source |
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| 166 | The Future of Pharma Through the Lens of Blockchain and AI Technology | Imran Rashid | Poster: Blockchain and AI are two key technologies which can solve century-old problems of trust, traceability, auditability and automation. Which will ignite innovative change in the pharma landscape. This poster is aimed towards providing a quick knowledge base of both technologies and impacts on the drug development process. Along with details towards how one will prove to be the stepping stone for the other. Present Challenges Slow enrollment Centralized data Data integrity Data validity Process overhead Traceability Audit trail overhead Reproducibility Future State Patient Centric Remote Trials Virtual Sites eConsent | source | 1 | |
| 167 | The Importance of General Linear Models (GLMs) in Clinical Research | Ritesh Dhimmar, Venkata Ikkurthy |
A fully randomized clinical trial is necessary to conduct unbiased and objective research. However, randomization can also hide some relations between study subjects that could be interesting to the investigator. Classification and similarity techniques are a powerful tool to discover these lost relations. Among these, general linear models help to find the effects between the treatment groups and within treatment groups. This paper provides an example of General linear model, ANOVA (Analysis of Variance) for different treatment groups. Analyzing the data between the treatment groups and within the groups using PROC GLM. Keywords: ANOVA, Least squares, SAS, PROC GLM, Regression, Interaction effect, linear. Mathematical Approach |
proc reg, regression analysis, simple linear regression, anova, proc genmod, proc glm, ancova, clinical trial, proc step, covariance, sample | source | 12 |
| 168 | The JumpStart Traceability Assessment | Tejas Patel, Sarah Ferkov |
Analysis Data Model (ADaM) datasets are frequently used as primary data for U.S. Food and Drug Administration (FDA) clinical reviews. Tracing ADaM data to its Study Data Tabulation Model (SDTM) source allows FDA reviewers to assess the relationship between the original and the derived data. The SDTM to ADaM Traceability Assessment investigates SDTM Demographics, Adverse Events, Laboratory, and Vital Signs datasets and compares them to their ADaM counterparts. The JumpStart analyst uses SAS programs and various display tools such as JMP to identify and evaluate differences between SDTM and ADaM. The customized SAS programs are run at the study level and matched to the submitted variables. Similarities and differences for key variables are then summarized and presented to the clinical reviewer. This paper discusses common findings from the traceability assessments | adam dataset, adverse events, record count, trial elements, sdtm domain, imputation, vital signs, screening period, cdisc | source | 10 |
| 169 | The Misery of the Miscellaneous Folder in Module 5 | Marguerite Kolb | Preparing regulatory submissions packages in the pharmaceutical industry can be very challenging due to the large amount of work and the need to research and understand regulatory and CDISC submission requirements. This paper aims to help those involved in placing the submission documents in the appropriate Module 5 folders by providing information and recommendation based on experience with the Misc folder within this Module. The paper will cover the following topics with regards to the Misc folder in Module 5: Regulatory requirements for CDISC submissions Best practices for preparing CDISC submission data packages Use Cases Frequently Asked Questions * Resources for more information | regulatory submission, ctcae grade, data cleaning, adam dataset, cdisc | source presentation source |
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| 170 | Titles Are a Programmer's Best Friend | Gregory Nunn | When creating outputs for clinical reporting, much of the information a Clinical Programmer requires is contained within the title. An experienced programmer will break the title down into components and use these components to visualise the output. So why don't we use these components to produce the output? This paper looks at building a bridge between an output title and an existing standardized reporting macro. By breaking the title down into recognisable components that can be used to populate macro variables, we can produce programs which create a 'best guess' at the output, alongside editable SAS code. Furthermore, by utilizing methods such as artificial intelligence, these programs could be adapted over time incorporating changes made to the tables. In this paper we explore the feasibility of these ideas and consider potential benefits and drawbacks | macro variable, change from baseline, subgroup analysis, vital signs | source poster source |
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| 171 | Untangling the Subject Elements Domain | Christine McNichol | The Subject Elements (SE) domain is unique and challenging in its sources and mapping. Without much encouragement, a map of the sources, derivations and interaction between records in SE can start to look more like a tangled mass of spaghetti than common linear SDTM mapping. Why is this? SE can have multiple sources for the data points needed to derive each element's start and end. Compared to other domains, SE also has a good deal more direct correlation to values in other domains. For successful implementation, it is critical to understand SE's purpose and requirements, the unique mapping path from source to SE and how this differs from other common domains, the steps needed to successfully derive SE, and programming methods that can be used. This paper will explore the inner workings of SE and explain how to successfully create SE one manageable bite at a time | raw data, date variable, sdtm domain, sdtm programming, randomization, medical history, cdisc | source presentation source |
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| 172 | Update on the PHUSE Standard Analyses & Code Sharing Working Group | Jane E. Marrer | This poster will provide an update on the efforts of the 6 project teams in the Standard Analysis and Code Sharing Working Group. The PHUSE Standard Analyses and Code Sharing Working Group provides recommendations for analyses, tables, figures, and listings for data that are common across therapeutic areas * Online platform called PHUSE Education (education.phuse.eu) for sharing code using GitHub: Contains a wealth of scripts that have been written by PHUSE members or developed and donated by the FDA and other organizations. - Crowd-sourcing code development will enable consistent interpretation of methods and substantial savings in resourcing across the industry. Code Sharing (Repository) Test Dataset Factory Best Practices for Quality Control and Validation GPP in Macro Development Analysis and Display White Papers Communication, Promotion, and Education Gap | macro definition, clinical trial | source poster source |
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| 173 | Using Graph Technology for Metadata-driven Clinical Development | Frederik Malfait | data standard, clinical data, clinical trial, clinical research | source | 6 | |
| 174 | Using Machine Learning for Automated Monitoring of Social Media for Pharmacovigilance | Rohit Banga | Social media platforms and public health forums provide important avenues for patients to discuss their health, use of medicinal products and any side effects. These avenues can provide important data points for pharmaceutical companies for safety monitoring. However, trying to manually find safety signals in vast unstructured data presents logistical challenges. We propose a novel approach for automated monitoring of social media for safety signals using cloud solutions. In this paper we will implement a Safety Tracking Tool for real time ingestion of data from Twitter. We will describe access patterns to ingest social media data in streaming/batch mode, extract relevant medical entities from unstructured data using a machine learning algorithm, and an automated software driven workflow that performs tasks such as sending notifications, automated reporting, and dashboard visualizations | adverse events, amazon s3, machine learning, data dictionary | source presentation source |
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| 175 | Using RWD in the Form of EHR Data to Predict Adverse Events and Outcomes | Corey Young, Susan Olson |
machine learning, insurance risk score, proc hpforest, adverse events | source | 4 | |
| 176 | Using Sankey Diagrams to Analyze Drug Pipelines | Tanmay Khole | Presentation | source | 18 | |
| 177 | Using Standardization and Automation to Streamline Generation of SDTM/ADaM Codelists | Eric Du | Generating SDTM and ADaM Controlled Terminology (CT) for Define-XML is currently a semi-manual process at Gilead. Due to the sheer amount of manual interventions, the process is prone to human errors, and may cannibalize manhours from more productive activities. We aim to establish a new process to increase efficiency by standardization and automation, relying on a centrally managed Standard CT. This Standard CT file will serve as a lookup table and be compiled from multiple standard sources, while also allowing for regular updates as NCI CT or Data Collection standards evolve. We will also present a new automation tool to generate study-specific SDTM/ADaM CT that can be manually customized, if necessary for a given study, and then ingested by Gilead's modified Define- XML v2.0 generation tool | excel, adam dataset, cdisc | source | 6 |
| 178 | Validation Consistency and Conformance Checking of SEND Datasets | Isaac Mativo, Mohit Matthew |
Validators can check if nonclinical studies submitted in CDISC SEND format meet FDA business rules, FDA validation rules, CDISC conformance rules and PMDA's Define.xml rules as applied for SEND. However, these validators cannot check if the SEND Trial Design is modeled correctly according to the Study's design w ith sufficient granularity. For example, if a Latin square study is coded in SEND as a parallel design, the SEND dataset cannot be reviewed or analyzed. Since SEND datasets are prepared separ ately using a CDISC trial design, the SEND data set should be checked to ensure it will generate the same summary results as listed in the audited study report. Manual "spot checks" are insufficient and risky. This paper discusses the best practices for sponsors to make sure that their SEND data set is of high quality a nd is consistent and conformant with the Study Report using special tools and validators | standard deviation, study day, data validation rules, cdisc | source presentation source |
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| 179 | Virtual Assistant for Conversational Experience with Clinical Data | Amit Gulwadi, Srinivasan Anandakumar |
Clinical and scientific data collected from Electronic Data Capture (EDC) systems and third -party sources (lab data, biomark er and PK/PD data) during clinical trial s involves intensive data exploration, visualization and analysis for ensuring data quality and patient safety. Current approaches typicall y utilize visualization tools where end users must navigate through multiple reports and dashboards to get the necessary insights. These reports and dashboards involve significant effort in building and integrating for every study, since they are typically integrated with study-specific data containers. An AI- based virtual assistant trained on both natural language and clinical trial standards (such as SDTM) enables the next generation conversational experience with clinical trial data | clinical trial, clinical data, analytics platform, proof of concept, machine learning, data ingestion, cdisc | source | 6 |
| 180 | Visualizing clinical operational metrics to enable decision-making and accountability | Michael Gonzales | Study milestone cycle time metrics are critical to understanding the health of a study, a department, and even a company. There are many ways to track, report, and visualize metrics, ranging from raw percentages to traffic lights. We developed a visualization scheme for fifteen key metrics, some of which are part of PHUSE's common Data Reporting Metrics1. In order to maximize the effectiveness of these visualizations, a dashboard was created using a popular tool, Tableau. Equally as important, and as difficult, was the development of a process surrounding the use of this dashboard. In this paper, we will detail the visualization scheme and the process while answering the following key questions: ➢ What impact does the dashboard and visualization choices have on the audience? ➢ What were the lessons learned? ➢ Where do we go from here? | database lock, data science | source presentation source |
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| 181 | Visualizing Clinical Trial Design and Results in 20 Graphical Displays - Achieving 20-20 in 2020 | Munish Mehra | With the increasing complexity and amount of data collected in clinical trials there is an explosion in the numbers of Statistical Tables, Figures and Listings (TFLs) generated. Reviewing thousands of pages of TFLs is challenging for Statistical Programmers, Biostatisticians, Clinicians, Medical Writers and others involved in generating, reviewing and summarizing trial design, conduct and results in a clinical study report (CSR). There is a need to present key clinical trial results visually and efficiently. This paper provides an initial approach to do this through 20 graphical displays. The displays include a variety of layout and focus on summarizing the most important information succinctly | clinical trial, clinical trial protocol | source presentation source |
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| 182 | Visualizing the Complexities of Genomics Datasets | Vibha Raghavan | forest plot, proc freq | source presentation source |
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| 183 | Visualizing Tumor Response Data Using ggplot2 R Package with Examples | Christine S Teng | ggplot2 is a widely used graphics library in R. Plots created using ggplot2 are built using a layered approach. Users can view the work in progress without needing to complete the final work. The code can be developed and executed one layer at a time and adjusted as needed. The purpose of this paper is to utilize the ggplot2 package and few add-on packages to produce multi-layered plots. Most of the examples require just a few lines of code. Oncology tumor response data will be used as input. Users can easily visualize the data and the outcome of tumor assessment over time, using information such as tumor size, new lesion, overall response, etc. with different plot arguments (ex: symbol, color, size, etc.). Many default settings of ggplot2 functions can generate high-quality plots. The examples illustrated in this paper will show that ggplot2 syntax is intuitive and easy to follow. SAS9, Windows, Intermediate Level Key Words: R, ggplot2, RECIST, Oncology | swimmer plot, data visualization | source presentation source |
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| 184 | Volcano Plot: Demystifying Why and How | Chandana Sudini, Jyothi Nelakurthi |
Volcano plot is yet another two-dimensional space saving scatter plot to graphically present measure of strength versus significance of a statistical signal. It may be used to interpret multiplicity adjustments to reduce false positives. When events (AE) are displayed by time interval, it illustrates how risk changes over the course of a trial. It is widely used in genomics to analyze microarray expression data etc. It is a powerful tool to visually display the statistical significance as p-values on Y-axis against metrics like relative risk diff erence, odds ratio, or hazard ratio on X -axis for a given treatment comparison. This paper talks about the interpretation of volcano plot in safety evaluation and construction of graphic elements using SGPLOT procedure statements by providing an example from a late stage clinical trial. Key Words: Volcano Plot, SGPLOT, Safety Evaluation |
volcano plot, adverse events, proc sgplot, proc step, preferred term, treatment arm, bubble plot, concomitant medications | source presentation source |
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| 185 | What Do Sponsors and CRO Programmers Think About Each Other and Their Primary Challenges? | Beena Josephine Rajeshwaran | The purpose of the presentation is to share insights to build stronger collaboration of programmers between CROs and their sponsors. This will lead to long-standing, shared partnerships through the shared commitment to help patients. There are two parts of this presentation. First, we address some common misconceptions around timelines and delivery where the related misunderstandings might result in frustrations from both sides, and how this can be avoided. In the second part of the presentation, we identify a list of roles, responsibilities and challenges we face as programmers from both Pharma and CROs. This can help programmers understand the expectations and come up with strategic programming plans to work in collaboration with each other and achieve the timeline with minimal budget and higher quality | data cleaning, sas programmer | source presentation source |
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| 186 | What You Need to Know About Submitting Data to the FDA | Pritesh Solanki, Janet Low |
Sponsors need to stay current with FDA requirements and plan to ensure that submitted electronic data are compliant with FDA regulations and review expectations. The focus of this paper is to share lessons learned while preparing clinical tabulations, analysis and site selection packages for Module 5 submission and to address key differences between CBER and CDER. Careful attention must be given to the requirement start dates as these can have significant impact on SDTM and ADaM implementation. FDA guidance continues to change which affects submission throughout the product development lifecycle. Regulatory communication such as waiver requests for data standards and meetings are outlets for sponsors to capitalize on the agreements prior to submission. The lack of data standards awareness may result in delays especially in times where FDA is enforcing technical rejection. How much do you know about submitting data to FDA? | data standard, clinical trial, cdisc | source presentation source |
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